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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<div style=\"float: right; padding: 20px 0px 0px 20px;\"><img src=\"images/macho-man.jpg\" width=\"500px\"></div>\n",
      "# Mach-O Binary Data Analysis\n",
      "<font size=4>In this notebook we're going to explore, understand, and classify Mach-O files as being 'benign' or 'malicious'.  We'll start by talking how we can extract the artifacts we are interested in.  Then we will explore the data, apply machine learning algorithms to the data, and the finally use those results to further explore the data.\n",
      "<br><br>\n",
      "** DISCLAIMER:** This exercise is for illustrative purposes and only uses about 500 samples which is too small for a generalizable model.\n",
      "</font>\n",
      "### References\n",
      "<font size=4>\n",
      "<ul>\n",
      "<li>https://developer.apple.com/library/mac/documentation/DeveloperTools/Conceptual/MachORuntime/Reference/reference.html</li>\n",
      "<li>https://developer.apple.com/library/mac/documentation/DeveloperTools/Conceptual/MachORuntime/Mach-O_File_Format.pdf</li>\n",
      "<li>http://en.wikipedia.org/wiki/Mach-O</li>\n",
      "</ul>\n",
      "</font>\n",
      "### Python Modules Used:\n",
      "<font size=4>\n",
      "<ul>\n",
      "<li>macholib: https://pythonhosted.org/macholib/\n",
      "<li>Pandas: Python Data Analysis Library (http://pandas.pydata.org)\n",
      "<li>Scikit Learn (http://scikit-learn.org) Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.\n",
      "<li>Matplotlib: Python 2D plotting library (http://matplotlib.org)\n",
      "</ul>\n",
      "</font>\n",
      "### Data generation\n",
      "<font size=4>\n",
      "All data for this talk was generated using Artifactr, a highly scalable analysis engine.  For more information, go to http://www.clicksecurity.com. </ shamless work plug >\n",
      "</font>\n",
      "### IPython Notebook for this talk: http://clicksecurity.github.io/data_hacking\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<div style=\"float: right; padding: 20px 0px 0px 20px;\"><img src=\"images/macho-layout.png\" width=\"400px\"></div>\n",
      "## Mach-O Basic Layout\n",
      "### Basic Structure\n",
      "<font size=4>A Mach-O file contains three major regions:\n",
      "<ol>\n",
      "<li>Header Structure\n",
      "<li>Load Commands\n",
      "<li>Segments\n",
      "</ol>\n",
      "</font>\n",
      "### Header Structure\n",
      "<font size=4>\n",
      "This structure identifies the file as a Mach-O file. The header also contains other basic file type information, indicates the target architecture, and contains flags specifying options that affect the interpretation of the rest of the file.\n",
      "</font>\n",
      "### Load Commands\n",
      "<font size=4>\n",
      "Directly following the header are a series of variable-size load commands that specify the layout and linkage characteristics of the file. Among other information, the load commands can specify:\n",
      "<ul>\n",
      "<li>The initial layout of the file in virtual memory</li>\n",
      "<li>The location of the symbol table (used for dynamic linking)</li>\n",
      "<li>The initial execution state of the main thread of the program</li>\n",
      "<li>The names of shared libraries that contain definitions for the main executable\u2019s imported symbols</li>\n",
      "</ul>\n",
      "</font>\n",
      "### Segments\n",
      "<font size=4>\n",
      "All Mach-O files contain one or more segments. Each segment contains zero or more sections. Each section of a segment contains code or data. Each segment defines a region of virtual memory that the dynamic linker maps into the address space of the process. The exact number and layout of segments and sections is specified by the load commands and the file type.\n",
      "</font>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "source": [
      "<div style=\"float: right; padding: 20px 0px 0px 20px;\"><img src=\"images/macholib-example.png\" width=\"400px\"></div>\n",
      "## macholib\n",
      "<font size=4>\n",
      "Current owner: Ronald Oussoren<br/>\n",
      "Description: macholib can be used to analyze and edit Mach-O headers, the executable format used by Mac OS X.<br>\n",
      "<br/>\n",
      "Wasn't the easiest to get data from each load command and other data I was interested in.  So we forked repo and added that ability.<br/>\n",
      "<br/>\n",
      "https://bitbucket.org/trogdorsey/macholib\n",
      "</font>\n",
      "<br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Imports and plot defaults"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "print 'pandas version is', pd.__version__\n",
      "import numpy as np\n",
      "print 'numpy version is', np.__version__\n",
      "import sklearn\n",
      "print 'scikit-learn version is', sklearn.__version__\n",
      "import matplotlib\n",
      "print 'matplotlib version is', matplotlib.__version__\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "pandas version is 0.13.1\n",
        "numpy version is 1.8.1\n",
        "scikit-learn version is"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 0.14.1\n",
        "matplotlib version is 1.3.1\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Plotting defaults and helper functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "plt.rcParams['font.size'] = 18.0\n",
      "plt.rcParams['figure.figsize'] = 16.0, 5.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_cm(cm, labels):\n",
      "    # Compute percentanges\n",
      "    percent = (cm*100.0)/np.array(np.matrix(cm.sum(axis=1)).T)\n",
      "    print 'Confusion Matrix Stats'\n",
      "    for i, label_i in enumerate(labels):\n",
      "        for j, label_j in enumerate(labels):\n",
      "            print \"%s/%s: %.2f%% (%d/%d)\" % (label_i, label_j, (percent[i][j]), cm[i][j], cm[i].sum())\n",
      "\n",
      "    # Show confusion matrix\n",
      "    # Thanks to kermit666 from stackoverflow\n",
      "    fig = plt.figure()\n",
      "    ax = fig.add_subplot(111)\n",
      "    ax.grid(b=False)\n",
      "    cax = ax.matshow(percent, cmap='coolwarm',vmin=0,vmax=100)\n",
      "    plt.title('Confusion matrix of the classifier')\n",
      "    fig.colorbar(cax)\n",
      "    ax.set_xticklabels([''] + labels)\n",
      "    ax.set_yticklabels([''] + labels)\n",
      "    plt.xlabel('Predicted')\n",
      "    plt.ylabel('True')\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Function to extract out the features we are interested with from the json blob"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def extract_features(data):\n",
      "    all_features = []\n",
      "    if not 'error' in data['characteristics']['macho']:\n",
      "        for i in range(data['characteristics']['macho']['number of architectures']):\n",
      "            features = {}\n",
      "            features['entropy'] = data['metadata']['entropy']\n",
      "            features['file_size'] = data['metadata']['file_size']\n",
      "            features['number of architectures'] = data['characteristics']['macho']['number of architectures']\n",
      "            features['header size'] = data['characteristics']['macho']['header'][i]['size']\n",
      "            features['cmd count'] = data['characteristics']['macho']['header'][i]['mach header']['ncmds']\n",
      "            features['cmd size'] = data['characteristics']['macho']['header'][i]['mach header']['sizeofcmds']\n",
      "            features['cputype'] = data['characteristics']['macho']['header'][i]['mach header']['cputype_string']\n",
      "            for flag in data['characteristics']['macho']['header'][i]['mach header']['flags']:\n",
      "                features[flag['name']] = 1\n",
      "            features['string count'] = data['verbose']['macho']['header'][i]['const strings count']\n",
      "            if 'const strings' in data['verbose']['macho']['header'][i]:\n",
      "                features['const strings'] = data['verbose']['macho']['header'][i]['const strings']\n",
      "            else:\n",
      "                features['const strings'] = []\n",
      "                \n",
      "            if 'symbol table strings' in data['verbose']['macho']['header'][i]:\n",
      "                features['symbol table strings'] = data['verbose']['macho']['header'][i]['symbol table strings']\n",
      "            else:\n",
      "                features['symbol table strings'] = []\n",
      "            features['segment names'] = []\n",
      "            features['section names'] = []\n",
      "            for command in data['verbose']['macho']['header'][i]['commands']:\n",
      "                if command['cmd_name'] in ['LC_SEGMENT', 'LC_SEGMENT_64']:\n",
      "                    features['segment names'].append(command['segname'])\n",
      "                    if command['segname'] == '__PAGEZERO':\n",
      "                        features['pz_size'] = command['cmd_size']\n",
      "                        features['pz_vmsize'] = command['vmsize']\n",
      "                        features['pz_vmaddr'] = command['vmaddr']\n",
      "                        features['pz_flags'] = command['flags']\n",
      "                        features['pz_filesize'] = command['filesize']\n",
      "                        features['pz_nsects'] = command['nsects']\n",
      "                        features['pz_fileoff'] = command['fileoff']\n",
      "                        for flag in command['initprot']:\n",
      "                            features['pz_initprot_'+flag] = 1\n",
      "                        for flag in command['maxprot']:\n",
      "                            features['pz_maxprot_'+flag] = 1\n",
      "                    if command['segname'] == '__TEXT':\n",
      "                        features['text_size'] = command['cmd_size']\n",
      "                        features['text_vmsize'] = command['vmsize']\n",
      "                        features['text_vmaddr'] = command['vmaddr']\n",
      "                        features['text_flags'] = command['flags']\n",
      "                        features['text_filesize'] = command['filesize']\n",
      "                        features['text_nsects'] = command['nsects']\n",
      "                        features['text_fileoff'] = command['fileoff']\n",
      "                        for flag in command['initprot']:\n",
      "                            features['text_initprot_'+flag] = 1\n",
      "                        for flag in command['maxprot']:\n",
      "                            features['text_maxprot_'+flag] = 1\n",
      "                        features['text_entropy'] = command['entropy']\n",
      "                        for section in command['sections']:\n",
      "                            if section['sectname'] == '__text':\n",
      "                                features['text_section_reloff'] = section['reloff']\n",
      "                                features['text_section_addr'] = section['addr']\n",
      "                                features['text_section_align'] = section['align']\n",
      "                                features['text_section_nreloc'] = section['nreloc']\n",
      "                                features['text_section_offset'] = section['offset']\n",
      "                                features['text_section_size'] = section['size']\n",
      "                                features['text_section_reserved1'] = section['reserved1']\n",
      "                                features['text_section_reserved2'] = section['reserved2']\n",
      "                                features['text_section_' + section['flags']['type']] = 1\n",
      "                                if 'attributes' in section['flags']:\n",
      "                                    for attr in section['flags']['attributes']:\n",
      "                                        features['text_section_flag_attribute_'+attr] = 1\n",
      "                            if section['sectname'] == '__const':\n",
      "                                features['const_section_reloff'] = section['reloff']\n",
      "                                features['const_section_addr'] = section['addr']\n",
      "                                features['const_section_align'] = section['align']\n",
      "                                features['const_section_nreloc'] = section['nreloc']\n",
      "                                features['const_section_offset'] = section['offset']\n",
      "                                features['const_section_size'] = section['size']\n",
      "                                features['const_section_reserved1'] = section['reserved1']\n",
      "                                features['const_section_reserved2'] = section['reserved2']\n",
      "                                features['const_section_' + section['flags']['type']] = 1\n",
      "                                if 'attributes' in section['flags']:\n",
      "                                    for attr in section['flags']['attributes']:\n",
      "                                        features['const_section_flag_attribute_'+attr] = 1\n",
      "                    if command['segname'] == '__DATA' and command['nsects'] > 0:\n",
      "                        features['data_size'] = command['cmd_size']\n",
      "                        features['data_vmsize'] = command['vmsize']\n",
      "                        features['data_vmaddr'] = command['vmaddr']\n",
      "                        features['data_flags'] = command['flags']\n",
      "                        features['data_filesize'] = command['filesize']\n",
      "                        features['data_nsects'] = command['nsects']\n",
      "                        features['data_fileoff'] = command['fileoff']\n",
      "                        for flag in command['initprot']:\n",
      "                            features['data_initprot_'+flag] = 1\n",
      "                        for flag in command['maxprot']:\n",
      "                            features['data_maxprot_'+flag] = 1\n",
      "                        features['data_entropy'] = command['entropy']\n",
      "                    #if command['segname'] == '__IMPORT':\n",
      "                    #    features['import_size'] = command['cmd_size']\n",
      "                    #    features['import_vmsize'] = command['vmsize']\n",
      "                    #    features['import_vmaddr'] = command['vmaddr']\n",
      "                    #    features['import_flags'] = command['flags']\n",
      "                    #    features['import_filesize'] = command['filesize']\n",
      "                    #    features['import_nsects'] = command['nsects']\n",
      "                    #    features['import_fileoff'] = command['fileoff']\n",
      "                    #    for flag in command['initprot']:\n",
      "                    #        features['import_initprot_'+flag] = 1\n",
      "                    #    for flag in command['maxprot']:\n",
      "                    #        features['import_maxprot_'+flag] = 1\n",
      "                    #    features['import_entropy'] = command['entropy']\n",
      "                    if command['segname'] == '__LINKEDIT':\n",
      "                        features['linkedit_size'] = command['cmd_size']\n",
      "                        features['linkedit_vmsize'] = command['vmsize']\n",
      "                        features['linkedit_vmaddr'] = command['vmaddr']\n",
      "                        features['linkedit_flags'] = command['flags']\n",
      "                        features['linkedit_filesize'] = command['filesize']\n",
      "                        features['linkedit_nsects'] = command['nsects']\n",
      "                        features['linkedit_fileoff'] = command['fileoff']\n",
      "                        for flag in command['initprot']:\n",
      "                            features['linkedit_initprot_'+flag] = 1\n",
      "                        for flag in command['maxprot']:\n",
      "                            features['linkedit_maxprot_'+flag] = 1\n",
      "                    \n",
      "                    if 'sections' in command:\n",
      "                        for section in command['sections']:\n",
      "                            features['section names'].append(section['sectname'])\n",
      "                if command['cmd_name'] == 'LC_SYMTAB':\n",
      "                    features['strsize'] = command['strsize']\n",
      "                    features['stroff'] = command['stroff']\n",
      "                    features['symoff'] = command['symoff']\n",
      "                    features['nsyms'] = command['nsyms']\n",
      "                if command['cmd_name'] in ['LC_DYLD_INFO_ONLY', 'LC_DYLD_INFO']:\n",
      "                    features['lazy_bind_size'] = command['lazy_bind_size']\n",
      "                    features['rebase_size'] = command['rebase_size']\n",
      "                    features['weak_bind_size'] = command['weak_bind_size']\n",
      "                    features['lazy_bind_off'] = command['lazy_bind_off']\n",
      "                    features['export_off'] = command['export_off']\n",
      "                    features['export_size'] = command['export_size']\n",
      "                    features['bind_off'] = command['bind_off']\n",
      "                    features['rebase_off'] = command['rebase_off']\n",
      "                    features['bind_size'] = command['bind_size']\n",
      "                    features['weak_bind_off'] = command['weak_bind_off']\n",
      "                if command['cmd_name'] == 'LC_DYSYMTAB':\n",
      "                    features['nextdefsym'] = command['nextdefsym']\n",
      "                    features['extreloff'] = command['extreloff']\n",
      "                    features['nlocrel'] = command['nlocrel']\n",
      "                    features['indirectsymoff'] = command['indirectsymoff']\n",
      "                    features['modtaboff'] = command['modtaboff']\n",
      "                    features['iundefsym'] = command['iundefsym']\n",
      "                    features['ntoc'] = command['ntoc']\n",
      "                    features['ilocalsym'] = command['ilocalsym']\n",
      "                    features['nundefsym'] = command['nundefsym']\n",
      "                    features['nextrefsyms'] = command['nextrefsyms']\n",
      "                    features['locreloff'] = command['locreloff']\n",
      "                    features['nmodtab'] = command['nmodtab']\n",
      "                    features['nlocalsym'] = command['nlocalsym']\n",
      "                    features['tocoff'] = command['tocoff']\n",
      "                    features['extrefsymoff'] = command['extrefsymoff']\n",
      "                    features['nindirectsyms'] = command['nindirectsyms']\n",
      "                    features['iextdefsym'] = command['iextdefsym']\n",
      "                    features['nextrel'] = command['nextrel']\n",
      "\n",
      "            features.update(data['verbose']['macho']['header'][i]['command type count'])\n",
      "            if 'LC_SEGMENT' in features:\n",
      "                features['number of segments'] = features['LC_SEGMENT']\n",
      "            else:\n",
      "                features['number of segments'] = features['LC_SEGMENT_64']\n",
      "            if 'entry point' in data['verbose']['macho']['header'][i]:\n",
      "                for key, value in data['verbose']['macho']['header'][i]['entry point'].iteritems():\n",
      "                    features['entry point ' + key] = value\n",
      "            all_features.append(features)\n",
      "\n",
      "    return all_features"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_files(file_list):\n",
      "    import json\n",
      "    features_list = []\n",
      "    for filename in file_list:\n",
      "        with open(filename,'rb') as f:\n",
      "            features = extract_features(json.loads(f.read()))\n",
      "            features_list.extend(features)\n",
      "    return features_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####The set of good files came from my MacBook Pro running Mavericks."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Good files\n",
      "import glob\n",
      "good_list = glob.glob('data/good/*.results')\n",
      "good_features = load_files(good_list)\n",
      "print \"Files:\", len(good_list)\n",
      "print \"Number of feature vectors:\", len(good_features)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Files: 266\n",
        "Number of feature vectors: 337\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####The set of bad files came from Contagio and VirusTotal"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Bad files\n",
      "bad_list = glob.glob('data/bad/*.results')\n",
      "bad_features = load_files(bad_list)\n",
      "print \"Files:\", len(bad_list)\n",
      "print \"Number of feature vectors:\", len(bad_features)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Files: 261\n",
        "Number of feature vectors: 302\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: right; padding: 20px 0px 0px 20px;\"><img src=\"images/randy-1.jpg\" width=\"400px\"></div>\n",
      "## Wait, I have more feature vectors than files?\n",
      "<font size=4>\n",
      "Mach-O supports fat binaries.  These are binaries that suport multiple architectures.  Can have both x86 and PowerPC architectures in both 32-bit and 64-bit.<br/>\n",
      "<br/>\n",
      "There are 3 (or 6 when you take into consideration endianness) different magic values for MachO binaries.  One (two) for 32-bit binaries, one (two) for 64-bit binaries, and one (two) for fat binaries.<br/>\n",
      "<br/>\n",
      "The magic value for fat binaries is cafebabe, which you might recognize as the magic value for java class files."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_good_orig = pd.DataFrame.from_records(good_features)\n",
      "df_good_orig['label'] = 'good'\n",
      "df_good_orig.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>LC_CODE_SEGMENT_SPLIT_INFO</th>\n",
        "      <th>LC_CODE_SIGNATURE</th>\n",
        "      <th>LC_DATA_IN_CODE</th>\n",
        "      <th>LC_DYLD_INFO</th>\n",
        "      <th>LC_DYLD_INFO_ONLY</th>\n",
        "      <th>LC_DYLIB_CODE_SIGN_DRS</th>\n",
        "      <th>LC_DYSYMTAB</th>\n",
        "      <th>LC_FUNCTION_STARTS</th>\n",
        "      <th>LC_ID_DYLIB</th>\n",
        "      <th>LC_LOAD_DYLIB</th>\n",
        "      <th>LC_LOAD_DYLINKER</th>\n",
        "      <th>LC_LOAD_WEAK_DYLIB</th>\n",
        "      <th>LC_MAIN</th>\n",
        "      <th>LC_REEXPORT_DYLIB</th>\n",
        "      <th>LC_RPATH</th>\n",
        "      <th>LC_SEGMENT</th>\n",
        "      <th>LC_SEGMENT_64</th>\n",
        "      <th>LC_SOURCE_VERSION</th>\n",
        "      <th>LC_SYMTAB</th>\n",
        "      <th>LC_UNIXTHREAD</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 9</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 9</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  4</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 148 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "   LC_CODE_SEGMENT_SPLIT_INFO  LC_CODE_SIGNATURE  LC_DATA_IN_CODE  \\\n",
        "0                         NaN                  1                1   \n",
        "1                         NaN                  1                1   \n",
        "2                         NaN                  1                1   \n",
        "3                         NaN                  1                1   \n",
        "4                         NaN                  1                1   \n",
        "\n",
        "   LC_DYLD_INFO  LC_DYLD_INFO_ONLY  LC_DYLIB_CODE_SIGN_DRS  LC_DYSYMTAB  \\\n",
        "0           NaN                  1                       1            1   \n",
        "1           NaN                  1                       1            1   \n",
        "2           NaN                  1                       1            1   \n",
        "3           NaN                  1                       1            1   \n",
        "4           NaN                  1                       1            1   \n",
        "\n",
        "   LC_FUNCTION_STARTS  LC_ID_DYLIB  LC_LOAD_DYLIB  LC_LOAD_DYLINKER  \\\n",
        "0                   1          NaN              1                 1   \n",
        "1                   1          NaN              9                 1   \n",
        "2                   1          NaN              9                 1   \n",
        "3                   1          NaN              5                 1   \n",
        "4                   1          NaN              1                 1   \n",
        "\n",
        "   LC_LOAD_WEAK_DYLIB  LC_MAIN  LC_REEXPORT_DYLIB  LC_RPATH  LC_SEGMENT  \\\n",
        "0                 NaN        1                NaN       NaN         NaN   \n",
        "1                 NaN        1                NaN       NaN         NaN   \n",
        "2                 NaN        1                NaN       NaN           4   \n",
        "3                 NaN        1                NaN       NaN         NaN   \n",
        "4                 NaN        1                NaN       NaN         NaN   \n",
        "\n",
        "   LC_SEGMENT_64  LC_SOURCE_VERSION  LC_SYMTAB  LC_UNIXTHREAD      \n",
        "0              4                  1          1            NaN ...  \n",
        "1              4                  1          1            NaN ...  \n",
        "2            NaN                  1          1            NaN ...  \n",
        "3              4                  1          1            NaN ...  \n",
        "4              4                  1          1            NaN ...  \n",
        "\n",
        "[5 rows x 148 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_good = df_good_orig\n",
      "for col in df_good.columns:\n",
      "    if col[0:3] in ['LC_', 'MH_']:\n",
      "        #print col\n",
      "        df_good[col].fillna(0, inplace=True)\n",
      "        \n",
      "df_good.fillna(-1, inplace=True)\n",
      "df_good_orig.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>LC_CODE_SEGMENT_SPLIT_INFO</th>\n",
        "      <th>LC_CODE_SIGNATURE</th>\n",
        "      <th>LC_DATA_IN_CODE</th>\n",
        "      <th>LC_DYLD_INFO</th>\n",
        "      <th>LC_DYLD_INFO_ONLY</th>\n",
        "      <th>LC_DYLIB_CODE_SIGN_DRS</th>\n",
        "      <th>LC_DYSYMTAB</th>\n",
        "      <th>LC_FUNCTION_STARTS</th>\n",
        "      <th>LC_ID_DYLIB</th>\n",
        "      <th>LC_LOAD_DYLIB</th>\n",
        "      <th>LC_LOAD_DYLINKER</th>\n",
        "      <th>LC_LOAD_WEAK_DYLIB</th>\n",
        "      <th>LC_MAIN</th>\n",
        "      <th>LC_REEXPORT_DYLIB</th>\n",
        "      <th>LC_RPATH</th>\n",
        "      <th>LC_SEGMENT</th>\n",
        "      <th>LC_SEGMENT_64</th>\n",
        "      <th>LC_SOURCE_VERSION</th>\n",
        "      <th>LC_SYMTAB</th>\n",
        "      <th>LC_UNIXTHREAD</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 148 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "   LC_CODE_SEGMENT_SPLIT_INFO  LC_CODE_SIGNATURE  LC_DATA_IN_CODE  \\\n",
        "0                           0                  1                1   \n",
        "1                           0                  1                1   \n",
        "2                           0                  1                1   \n",
        "3                           0                  1                1   \n",
        "4                           0                  1                1   \n",
        "\n",
        "   LC_DYLD_INFO  LC_DYLD_INFO_ONLY  LC_DYLIB_CODE_SIGN_DRS  LC_DYSYMTAB  \\\n",
        "0             0                  1                       1            1   \n",
        "1             0                  1                       1            1   \n",
        "2             0                  1                       1            1   \n",
        "3             0                  1                       1            1   \n",
        "4             0                  1                       1            1   \n",
        "\n",
        "   LC_FUNCTION_STARTS  LC_ID_DYLIB  LC_LOAD_DYLIB  LC_LOAD_DYLINKER  \\\n",
        "0                   1            0              1                 1   \n",
        "1                   1            0              9                 1   \n",
        "2                   1            0              9                 1   \n",
        "3                   1            0              5                 1   \n",
        "4                   1            0              1                 1   \n",
        "\n",
        "   LC_LOAD_WEAK_DYLIB  LC_MAIN  LC_REEXPORT_DYLIB  LC_RPATH  LC_SEGMENT  \\\n",
        "0                   0        1                  0         0           0   \n",
        "1                   0        1                  0         0           0   \n",
        "2                   0        1                  0         0           4   \n",
        "3                   0        1                  0         0           0   \n",
        "4                   0        1                  0         0           0   \n",
        "\n",
        "   LC_SEGMENT_64  LC_SOURCE_VERSION  LC_SYMTAB  LC_UNIXTHREAD      \n",
        "0              4                  1          1              0 ...  \n",
        "1              4                  1          1              0 ...  \n",
        "2              0                  1          1              0 ...  \n",
        "3              4                  1          1              0 ...  \n",
        "4              4                  1          1              0 ...  \n",
        "\n",
        "[5 rows x 148 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_bad_orig = pd.DataFrame.from_records(bad_features)\n",
      "df_bad_orig['label'] = 'bad'\n",
      "df_bad = df_bad_orig\n",
      "for col in df_bad.columns:\n",
      "    if col[0:3] in ['LC_', 'MH_']:\n",
      "        #print col\n",
      "        df_bad[col].fillna(0, inplace=True)\n",
      "        \n",
      "df_bad.fillna(-1, inplace=True)\n",
      "df_bad.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>LC_CODE_SIGNATURE</th>\n",
        "      <th>LC_DATA_IN_CODE</th>\n",
        "      <th>LC_DYLD_INFO</th>\n",
        "      <th>LC_DYLD_INFO_ONLY</th>\n",
        "      <th>LC_DYSYMTAB</th>\n",
        "      <th>LC_ENCRYPTION_INFO</th>\n",
        "      <th>LC_FUNCTION_STARTS</th>\n",
        "      <th>LC_ID_DYLIB</th>\n",
        "      <th>LC_LAZY_LOAD_DYLIB</th>\n",
        "      <th>LC_LOAD_DYLIB</th>\n",
        "      <th>LC_LOAD_DYLINKER</th>\n",
        "      <th>LC_SEGMENT</th>\n",
        "      <th>LC_SEGMENT_64</th>\n",
        "      <th>LC_SYMTAB</th>\n",
        "      <th>LC_TWOLEVEL_HINTS</th>\n",
        "      <th>LC_UNIXTHREAD</th>\n",
        "      <th>LC_UUID</th>\n",
        "      <th>LC_VERSION_MIN_IPHONEOS</th>\n",
        "      <th>LC_VERSION_MIN_MACOSX</th>\n",
        "      <th>MH_BINDATLOAD</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  8</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  8</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 11</td>\n",
        "      <td> 0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 153 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "   LC_CODE_SIGNATURE  LC_DATA_IN_CODE  LC_DYLD_INFO  LC_DYLD_INFO_ONLY  \\\n",
        "0                  0                0             1                  0   \n",
        "1                  0                0             1                  0   \n",
        "2                  1                0             0                  1   \n",
        "3                  1                0             0                  1   \n",
        "4                  1                1             0                  1   \n",
        "\n",
        "   LC_DYSYMTAB  LC_ENCRYPTION_INFO  LC_FUNCTION_STARTS  LC_ID_DYLIB  \\\n",
        "0            1                   0                   0            0   \n",
        "1            1                   0                   0            1   \n",
        "2            1                   0                   0            0   \n",
        "3            1                   0                   0            0   \n",
        "4            1                   0                   1            0   \n",
        "\n",
        "   LC_LAZY_LOAD_DYLIB  LC_LOAD_DYLIB  LC_LOAD_DYLINKER  LC_SEGMENT  \\\n",
        "0                   0              8                 1           0   \n",
        "1                   0              8                 0           0   \n",
        "2                   0              2                 1           0   \n",
        "3                   0              2                 1           4   \n",
        "4                   0             11                 0           4   \n",
        "\n",
        "   LC_SEGMENT_64  LC_SYMTAB  LC_TWOLEVEL_HINTS  LC_UNIXTHREAD  LC_UUID  \\\n",
        "0              4          1                  0              1        1   \n",
        "1              3          1                  0              0        1   \n",
        "2              4          1                  0              1        1   \n",
        "3              0          1                  0              1        1   \n",
        "4              0          1                  0              0        1   \n",
        "\n",
        "   LC_VERSION_MIN_IPHONEOS  LC_VERSION_MIN_MACOSX  MH_BINDATLOAD      \n",
        "0                        0                      0              0 ...  \n",
        "1                        0                      0              0 ...  \n",
        "2                        0                      0              0 ...  \n",
        "3                        0                      0              0 ...  \n",
        "4                        0                      1              0 ...  \n",
        "\n",
        "[5 rows x 153 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Let's explore the data.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_good['cputype'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x112d9ec90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AM/J6xnXx4sWaOHGiunTpoqCgIMXHx19wb3p6uoKCghp8/fa3v/XYX1tbqxdffFHdu3dX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\nXAEAAAAApkbjCgAAAAAwtf8P13wyQpJOoRUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112d9ec10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_bad['cputype'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x112db1cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Gjx+vAwcOKCcnx1uxAAAAAAAmd5W3Xqhfv35KTEzUnDlzFB0drfj4eJWWluqd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\nAAAAAMChMbgCAAAAABzaPwDErnQZpklZ1wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112e31710>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<font size =4>\n",
      "Let's get rid of the PowerPC cputype because that switch happened 9 years ago.  I think we can all agree that malware authors won't be targeting that CPU in the future.  And we probably want to examine malware for iOS devices separately.\n",
      "</font>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_all_orig = pd.concat([df_bad, df_good], ignore_index=True)\n",
      "df_all = df_all_orig\n",
      "for col in df_all.columns:\n",
      "    if col[0:3] in ['LC_', 'MH_']:\n",
      "        #print col\n",
      "        df_all[col].fillna(0, inplace=True)\n",
      "        \n",
      "df_all.fillna(-1, inplace=True)\n",
      "# Break out by cpu type\n",
      "cond = df_all['cputype'] == 'x86_64' \n",
      "df_x64 = df_all[cond]\n",
      "cond = df_all['cputype'] == 'i386'\n",
      "df_x86 = df_all[cond]\n",
      "df_all = pd.concat([df_x64, df_x86], ignore_index=True)\n",
      "#print df['symbol table strings']\n",
      "df = df_all.drop(['const strings', 'symbol table strings', 'segment names',\n",
      "                  'section names', 'entry point section name', 'entry point segment name',\n",
      "                  'entry point instruction type'], axis=1)\n",
      "df['cputype'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x112fe7b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8mlwALYHldAAAgM+wnA4AwGZea1xLS0slSV27dm3w2PljJSUlHvsjIiIUEBDQ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112e47d90>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Let's start to blindly explore the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cond = df['label'] == 'good'\n",
      "good = df[cond]\n",
      "bad  = df[~cond]\n",
      "bad['cmd count'].hist(alpha=.5,label='bad',bins=40)\n",
      "good['cmd count'].hist(alpha=.5,label='good',bins=40)\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.legend.Legend at 0x11338d1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Z2EEWdpCFHWQReShcAQAAAACmUbjCQS+AHWRhBz2utjA37CALO8jCDrKwgywi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\n10VW5x17AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112e55810>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.boxplot('cmd count', 'label')\n",
      "plt.xlabel('bad vs. good files')\n",
      "plt.ylabel('Number of Commands')\n",
      "plt.title('Comparision of Number of Commands')\n",
      "plt.suptitle('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.text.Text at 0x1135dca90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oRESV6kXHlRIRERFR5TFpz/Xq1auxa9cupKSkwMrKyuD13dzcMHbsWOTl5VX6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\natIaiSsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112fe9490>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.boxplot(column='cmd size', by='label')\n",
      "plt.xlabel('bad vs. good files')\n",
      "plt.ylabel('Command Size')\n",
      "plt.title('Comparision of Command Sizes')\n",
      "plt.suptitle(\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.text.Text at 0x11374e810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lPeI3JCIiIiIiInq0oKAgnDhxQuc+kybunTt3xqefforff/9da9/Vq1cBAHXq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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1135c3090>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Split the classes up so we can set colors, size, labels\n",
      "cond = df['label'] == 'good'\n",
      "good = df[cond]\n",
      "bad  = df[~cond]\n",
      "plt.scatter(good['cmd count'], good['cmd size'], \n",
      "            s=140, c='#aaaaff', label='Good', alpha=.4)\n",
      "plt.scatter(bad['cmd count'], bad['cmd size'], \n",
      "            s=40, c='r', label='Bad', alpha=.5)\n",
      "plt.legend()\n",
      "plt.xlabel('Command Count')\n",
      "plt.ylabel('Command Size')\n",
      "plt.title('Command Size Vs Command Count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x1138d4690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kMrz++uuqtGvXrgHQvsWHMi0jI0OtvI2NjdZ7uZycnHDr1i3VwkS6tk1ERERE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DAAAg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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112e34110>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.boxplot('entropy', 'label')\n",
      "plt.xlabel('bad vs. good files')\n",
      "plt.ylabel('Number of Commands')\n",
      "plt.title('Comparision of Entropy')\n",
      "plt.suptitle('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.text.Text at 0x113519b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f1NNPP13UmfLFpF8AAAAAAG/xyqRfFStWVPHixb0W6mJHjhzR0KFDVadOHZUo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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1135dc7d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.boxplot('number of segments', 'label')\n",
      "plt.xlabel('bad vs. good files')\n",
      "plt.ylabel('Number of Segments')\n",
      "plt.title('Comparision of Number of Segments')\n",
      "plt.suptitle('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.text.Text at 0x11412ce50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/XEePHlVAQIDbz5fZe3otfxuky0u2zZw5U7NmzVKrVq2cT18sW7ZM6enp6tOn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\nPFjvvvuu6tSpo44dO6pw4cJ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7XZIoU74lyvnz23V5pMbl8p8X6xtddtTl1PH/LIeFSefO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\nILEGAAAAAKAZ/g9YrfcG2Tjl/gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11410cd90>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cond = df['label'] == 'good'\n",
      "good = df[cond]\n",
      "bad  = df[~cond]\n",
      "plt.scatter(good['entropy'], good['number of segments'], \n",
      "            s=140, c='#aaaaff', label='Good', alpha=.4)\n",
      "plt.scatter(bad['entropy'], bad['number of segments'], \n",
      "            s=40, c='r', label='Bad', alpha=.5)\n",
      "plt.legend()\n",
      "plt.xlabel('Entropy')\n",
      "plt.ylabel('Number of Segments')\n",
      "plt.title('Number of Segments Vs Entropy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.text.Text at 0x1142b6790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OHW80adIJmzfvgkajMdrm/v37OHz4KJYv/xULFvyM5ct/xeHDR3H//v1KPU4A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\nqUw2IydBIgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113737d50>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### First we try classifying binaries just based on cmd count and cmd size"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# List of feature vectors (scikit learn uses 'X' for the matrix of feature vectors)\n",
      "X_cmd = df.as_matrix(['cmd count', 'cmd size'])\n",
      "\n",
      "# Labels (scikit learn uses 'y' for classification labels)\n",
      "y = np.array(df['label'].tolist())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Random Forest is a popular ensemble machine learning classifier.\n",
      "# http://scikit-learn.org/dev/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n",
      "#\n",
      "import sklearn.ensemble\n",
      "clf_cmd = sklearn.ensemble.RandomForestClassifier(n_estimators=50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now we can use scikit learn's cross validation to assess predictive performance.\n",
      "scores = sklearn.cross_validation.cross_val_score(clf_cmd, X_cmd, y, cv=10)\n",
      "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy: 0.89 (+/- 0.07)\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_seed = 1022\n",
      "my_tsize = .2\n",
      "from sklearn.cross_validation import train_test_split\n",
      "X_train, X_test, y_train, y_test = train_test_split(X_cmd, y, test_size=my_tsize, random_state=my_seed)\n",
      "clf_cmd.fit(X_train, y_train)\n",
      "clf_cmd_scores = clf_cmd.score(X_test, y_test)\n",
      "print(\"Accuracy: %0.2f\" % clf_cmd_scores)\n",
      "y_pred = clf_cmd.predict(X_test)\n",
      "\n",
      "from sklearn.metrics import confusion_matrix\n",
      "labels = ['good', 'bad']\n",
      "cm = confusion_matrix(y_test, y_pred, labels)\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy: 0.91\n",
        "Confusion Matrix Stats\n",
        "good/good: 92.75% (64/69)\n",
        "good/bad: 7.25% (5/69)\n",
        "bad/good: 11.63% (5/43)\n",
        "bad/bad: 88.37% (38/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LREQELl68iOzsbBQXF8Pc3Fw0Clqa/Px8hIWFISUlBfX19Xj99dexbt06qbOA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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114488310>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Now try number of segments and entropy.  This shouldn't work all that great."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf_2 = sklearn.ensemble.RandomForestClassifier(n_estimators=50)\n",
      "X_2 = df.as_matrix(['number of segments', 'entropy'])\n",
      "X_train, X_test, y_train, y_test = train_test_split(X_2, y, test_size=my_tsize, random_state=my_seed)\n",
      "clf_2.fit(X_train, y_train)\n",
      "y_pred = clf_2.predict(X_test)\n",
      "\n",
      "from sklearn.metrics import confusion_matrix\n",
      "labels = ['good', 'bad']\n",
      "cm = confusion_matrix(y_test, y_pred, labels)\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "good/good: 73.91% (51/69)\n",
        "good/bad: 26.09% (18/69)\n",
        "bad/good: 18.60% (8/43)\n",
        "bad/bad: 81.40% (35/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ioqJw8eJFZGdno7i4GBYWFqJR0NLk5+cjPDwcKSkpqK+vx+uvv461a9dKnQWs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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1142a9210>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Okay now try putting in ALL the features... except the label, which would be cheating.  And the cputype, which is a string."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf_all = sklearn.ensemble.RandomForestClassifier(n_estimators=50)\n",
      "no_label = list(df.columns.values)\n",
      "no_label.remove('label')\n",
      "no_label.remove('cputype')\n",
      "X = df.as_matrix(no_label)\n",
      "\n",
      "# 80/20 Split for predictive test\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=my_tsize, random_state=my_seed)\n",
      "clf_all.fit(X_train, y_train)\n",
      "y_pred = clf_all.predict(X_test)\n",
      "cm = confusion_matrix(y_test, y_pred, labels)\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "good/good: 92.75% (64/69)\n",
        "good/bad: 7.25% (5/69)\n",
        "bad/good: 6.98% (3/43)\n",
        "bad/bad: 93.02% (40/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SpUvIyspCUVERzMzMRKOgpcnLy0NYWBiSk5PR0NCAV199FRs2bJA6C1hzczM+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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1142b8690>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Feature Selection\n",
      "# Which features best deferentiated the two classes?\n",
      "# Here we're going to grab the feature_importances from the classifier itself, \n",
      "importances = zip(no_label, clf_all.feature_importances_)\n",
      "importances.sort(key=lambda k:k[1], reverse=True)\n",
      "for im in importances[0:20]:\n",
      "    print im[0].ljust(30), im[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "LC_UNIXTHREAD                  0.11111604246\n",
        "LC_CODE_SIGNATURE              0.0500117721965\n",
        "rebase_off                     0.0407851853834\n",
        "LC_MAIN                        0.0385643285089\n",
        "text_section_align             0.0339347105743\n",
        "LC_DYLIB_CODE_SIGN_DRS         0.0335313105805\n",
        "LC_DYLD_INFO_ONLY              0.0304556902729\n",
        "linkedit_filesize              0.029905256752\n",
        "MH_PIE                         0.0226196128034\n",
        "LC_SOURCE_VERSION              0.0198478375291\n",
        "pz_size                        0.0178616171746\n",
        "rebase_size                    0.017668781554\n",
        "text_section_addr              0.0159335856278\n",
        "data_entropy                   0.0156907971405\n",
        "LC_FUNCTION_STARTS             0.0150161007743\n",
        "data_size                      0.0149447133839\n",
        "linkedit_vmsize                0.014562058373\n",
        "const_section_size             0.0135111773331\n",
        "extreloff                      0.0132383705557\n",
        "LC_VERSION_MIN_MACOSX          0.0127062868088\n"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Closer look at important features\n",
      "<font size=4>\n",
      "The LC_UNIXTHREAD command defines the entry point into the executable.  In 10.8 and above, this was replaced by LC_MAIN.  As stated above, I grabbed my clean binaries from my laptop, which is running Mavericks (10.9).  The malware was grabbed from Contagio and VirusTotal, with no thought as to what version it was compiled on."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_good['LC_UNIXTHREAD'].value_counts().plot(kind='bar', label='good')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.legend.Legend at 0x11410c9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lcrnk8XiCnpszZ46/7zYmJkaDBw/2/+Kquxk147YZXy40pK+KDcaMr2Wcb1g8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\nVgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a0f2f10>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_bad['LC_UNIXTHREAD'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1155239d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AQxBagehz0zOuP/vZz3T+/HlJkt/v19WrV/W9731P0vVnvD788MPBvbW1tfJ6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\nAIxGcAUAAAAAGO3/Ab986kba8L2iAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11548fd10>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<font size=4>\n",
      "My clean set clearly has a sample bias towards LC_MAIN.  We do not want to overfit our model and since we would like our classifier to be agnostic to OS version, we will remove those labels.<br/>\n",
      "<br/>\n",
      "Over fitting problems: https://www.youtube.com/watch?v=DQWI1kvmwRg\n",
      "</font>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Now try all the same features as before, but without LC_UNIXTHREAD and LC_MAIN"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf_some = sklearn.ensemble.RandomForestClassifier(n_estimators=50)\n",
      "some = list(df.columns.values)\n",
      "some.remove('label')\n",
      "some.remove('cputype')\n",
      "some.remove('LC_UNIXTHREAD')\n",
      "some.remove('LC_MAIN')\n",
      "X_some = df.as_matrix(some)\n",
      "\n",
      "# 80/20 Split for predictive test\n",
      "X_train_some, X_test_some, y_train_some, y_test_some = train_test_split(X_some, y, test_size=my_tsize, random_state=my_seed)\n",
      "clf_some.fit(X_train_some, y_train_some)\n",
      "y_pred_some = clf_some.predict(X_test_some)\n",
      "cm = confusion_matrix(y_test_some, y_pred_some, labels)\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "good/good: 95.65% (66/69)\n",
        "good/bad: 4.35% (3/69)\n",
        "bad/good: 6.98% (3/43)\n",
        "bad/bad: 93.02% (40/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XkZmZicLCQpiZmYlGQUuTm5uL0NBQJCUloa6uDsOGDcOGDRukzgLW2NiIL7/8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\n3zpUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1154fe210>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<font size=4>\n",
      "Hey, it's the same(ish).  That's kinda nice.\n",
      "</font>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "importances = zip(some, clf_some.feature_importances_)\n",
      "importances.sort(key=lambda k:k[1], reverse=True)\n",
      "for im in importances[0:20]:\n",
      "    print im[0].ljust(30), im[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "rebase_off                     0.0833325758996\n",
        "text_section_align             0.0428642619275\n",
        "LC_SOURCE_VERSION              0.0414863657063\n",
        "LC_DYLIB_CODE_SIGN_DRS         0.0390404405528\n",
        "linkedit_filesize              0.0343569215656\n",
        "rebase_size                    0.0268211203307\n",
        "LC_FUNCTION_STARTS             0.0258331878885\n",
        "LC_DATA_IN_CODE                0.0257389166901\n",
        "LC_VERSION_MIN_MACOSX          0.0248798919781\n",
        "LC_CODE_SIGNATURE              0.0231596346234\n",
        "pz_size                        0.0230647740647\n",
        "LC_DYLD_INFO_ONLY              0.0227836821431\n",
        "MH_PIE                         0.0197788472089\n",
        "data_nsects                    0.0193926037336\n",
        "pz_vmaddr                      0.0180289427176\n",
        "lazy_bind_size                 0.0156826479795\n",
        "linkedit_vmsize                0.0142413695731\n",
        "cmd count                      0.0137541943859\n",
        "nlocrel                        0.0130961872487\n",
        "bind_off                       0.012773950971\n"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### What about the strings?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "symbol_strings = []\n",
      "for strings, label in zip(df_all['symbol table strings'], df_all['label']):\n",
      "    for symbol in strings:\n",
      "        symbol_strings.append({'symbol string': symbol, 'label': label})\n",
      "\n",
      "pd_symbols = pd.DataFrame.from_records(symbol_strings)\n",
      "pd_symbols.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>label</th>\n",
        "      <th>symbol string</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> bad</td>\n",
        "      <td>                 __mh_execute_header</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> bad</td>\n",
        "      <td>                _AuthorizationCreate</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> bad</td>\n",
        "      <td> _AuthorizationExecuteWithPrivileges</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> bad</td>\n",
        "      <td>                           _BIO_ctrl</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> bad</td>\n",
        "      <td>                       _BIO_f_base64</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "  label                        symbol string\n",
        "0   bad                  __mh_execute_header\n",
        "1   bad                 _AuthorizationCreate\n",
        "2   bad  _AuthorizationExecuteWithPrivileges\n",
        "3   bad                            _BIO_ctrl\n",
        "4   bad                        _BIO_f_base64\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "sys.path.insert(0,'..')\n",
      "import data_hacking.simple_stats as ss"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Spin up our g_test class\n",
      "g_test = ss.GTest()\n",
      "\n",
      "# Here we'd like to see how strongly various strings are assoicated with being clean or malware.\n",
      "df_ct, df_cd, df_symbol_stats = g_test.highest_gtest_scores(pd_symbols['symbol string'],\n",
      "                                                            pd_symbols['label'], N=0, matches=0, min_volume=5)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_symbol_stats.dropna(inplace=True)\n",
      "df_symbol_stats.sort('bad_g', ascending=0).head(25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
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        "    <tr>\n",
        "      <th>__ZN12_GLOBAL__N_12_5E</th>\n",
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        "      <td> 0</td>\n",
        "      <td> 1.000000</td>\n",
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        "      <td> 0</td>\n",
        "      <td> 1.000000</td>\n",
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        "  </tbody>\n",
        "</table>\n",
        "<p>25 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
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        "___cxx_global_var_init5                             86     0  1.000000   \n",
        "\n",
        "                                                   good_cd  total_cd  \\\n",
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        "___cxx_global_var_init9                           0.000000        86   \n",
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        "___cxx_global_var_init6                           0.000000        86   \n",
        "___cxx_global_var_init5                           0.000000        86   \n",
        "\n",
        "                                                      bad_exp        bad_g  \\\n",
        "H1\\x8d5A`\\x02                                     3570.141890  2935.283635   \n",
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        "___tcf_0                                            87.850886    56.894804   \n",
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        "___cxx_global_var_init2                             64.965361    53.412936   \n",
        "___cxx_global_var_init3                             64.965361    53.412936   \n",
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        "___cxx_global_var_init8                             63.488876    52.199006   \n",
        "__ZN12_GLOBAL__N_12_9E                              63.488876    52.199006   \n",
        "__ZN12_GLOBAL__N_12_2E                              63.488876    52.199006   \n",
        "___cxx_global_var_init9                             63.488876    52.199006   \n",
        "__ZN12_GLOBAL__N_12_8E                              63.488876    52.199006   \n",
        "___cxx_global_var_init11                            63.488876    52.199006   \n",
        "__ZN12_GLOBAL__N_12_7E                              63.488876    52.199006   \n",
        "___cxx_global_var_init6                             63.488876    52.199006   \n",
        "___cxx_global_var_init5                             63.488876    52.199006   \n",
        "\n",
        "                                                     good_exp     good_g  \n",
        "H1\\x8d5A`\\x02                                     1265.858110   0.000000  \n",
        "L\\x89\\xe7\\xff\\x158`\\x02                           1265.858110   0.000000  \n",
        "__ZStL8__ioinit                                     41.881162   0.000000  \n",
        "___tcf_0                                            31.149114 -19.764316  \n",
        "__ZN5boost6systemL14posix_categoryE                 24.081668   0.000000  \n",
        "__ZN5boost6systemL11native_ecatE                    24.081668   0.000000  \n",
        "__ZN5boost6systemL10errno_ecatE                     24.081668   0.000000  \n",
        "__GLOBAL__I_a                                       24.866940  -6.427078  \n",
        "___cxx_global_var_init                              23.558153   0.000000  \n",
        "__Z41__static_initialization_and_destruction_0ii    30.887357 -20.782115  \n",
        "___cxx_global_var_init2                             23.034639   0.000000  \n",
        "___cxx_global_var_init3                             23.034639   0.000000  \n",
        "___cxx_global_var_init4                             23.034639   0.000000  \n",
        "___cxx_global_var_init1                             23.034639   0.000000  \n",
        "__ZN12_GLOBAL__N_12_1E                              22.511124   0.000000  \n",
        "__ZN12_GLOBAL__N_12_5E                              22.511124   0.000000  \n",
        "___cxx_global_var_init8                             22.511124   0.000000  \n",
        "__ZN12_GLOBAL__N_12_9E                              22.511124   0.000000  \n",
        "__ZN12_GLOBAL__N_12_2E                              22.511124   0.000000  \n",
        "___cxx_global_var_init9                             22.511124   0.000000  \n",
        "__ZN12_GLOBAL__N_12_8E                              22.511124   0.000000  \n",
        "___cxx_global_var_init11                            22.511124   0.000000  \n",
        "__ZN12_GLOBAL__N_12_7E                              22.511124   0.000000  \n",
        "___cxx_global_var_init6                             22.511124   0.000000  \n",
        "___cxx_global_var_init5                             22.511124   0.000000  \n",
        "\n",
        "[25 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_symbol_stats.sort('good_g', ascending=0).head(25)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bad</th>\n",
        "      <th>good</th>\n",
        "      <th>bad_cd</th>\n",
        "      <th>good_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
        "      <th>bad_g</th>\n",
        "      <th>good_exp</th>\n",
        "      <th>good_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>dyld_stub_binder</th>\n",
        "      <td> 173</td>\n",
        "      <td> 276</td>\n",
        "      <td> 0.385301</td>\n",
        "      <td> 0.614699</td>\n",
        "      <td> 449</td>\n",
        "      <td> 331.470990</td>\n",
        "      <td>-224.986050</td>\n",
        "      <td> 117.529010</td>\n",
        "      <td> 471.251050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>radr://5614542</th>\n",
        "      <td> 107</td>\n",
        "      <td> 236</td>\n",
        "      <td> 0.311953</td>\n",
        "      <td> 0.688047</td>\n",
        "      <td> 343</td>\n",
        "      <td> 253.217260</td>\n",
        "      <td>-184.343670</td>\n",
        "      <td>  89.782740</td>\n",
        "      <td> 456.159232</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___stderrp</th>\n",
        "      <td>  35</td>\n",
        "      <td> 187</td>\n",
        "      <td> 0.157658</td>\n",
        "      <td> 0.842342</td>\n",
        "      <td> 222</td>\n",
        "      <td> 163.889888</td>\n",
        "      <td>-108.069271</td>\n",
        "      <td>  58.110112</td>\n",
        "      <td> 437.119579</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_strcmp</th>\n",
        "      <td>  51</td>\n",
        "      <td> 169</td>\n",
        "      <td> 0.231818</td>\n",
        "      <td> 0.768182</td>\n",
        "      <td> 220</td>\n",
        "      <td> 162.413403</td>\n",
        "      <td>-118.148571</td>\n",
        "      <td>  57.586597</td>\n",
        "      <td> 363.893795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___stack_chk_fail</th>\n",
        "      <td>  92</td>\n",
        "      <td> 190</td>\n",
        "      <td> 0.326241</td>\n",
        "      <td> 0.673759</td>\n",
        "      <td> 282</td>\n",
        "      <td> 208.184453</td>\n",
        "      <td>-150.261006</td>\n",
        "      <td>  73.815547</td>\n",
        "      <td> 359.272784</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___stack_chk_guard</th>\n",
        "      <td>  92</td>\n",
        "      <td> 190</td>\n",
        "      <td> 0.326241</td>\n",
        "      <td> 0.673759</td>\n",
        "      <td> 282</td>\n",
        "      <td> 208.184453</td>\n",
        "      <td>-150.261006</td>\n",
        "      <td>  73.815547</td>\n",
        "      <td> 359.272784</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_fprintf</th>\n",
        "      <td>  39</td>\n",
        "      <td> 156</td>\n",
        "      <td> 0.200000</td>\n",
        "      <td> 0.800000</td>\n",
        "      <td> 195</td>\n",
        "      <td> 143.957334</td>\n",
        "      <td>-101.864515</td>\n",
        "      <td>  51.042666</td>\n",
        "      <td> 348.564572</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___stdoutp</th>\n",
        "      <td>   8</td>\n",
        "      <td> 120</td>\n",
        "      <td> 0.062500</td>\n",
        "      <td> 0.937500</td>\n",
        "      <td> 128</td>\n",
        "      <td>  94.495071</td>\n",
        "      <td> -39.505698</td>\n",
        "      <td>  33.504929</td>\n",
        "      <td> 306.191801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__mh_execute_header</th>\n",
        "      <td> 218</td>\n",
        "      <td> 225</td>\n",
        "      <td> 0.492099</td>\n",
        "      <td> 0.507901</td>\n",
        "      <td> 443</td>\n",
        "      <td> 327.041534</td>\n",
        "      <td>-176.838162</td>\n",
        "      <td> 115.958466</td>\n",
        "      <td> 298.290746</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_printf</th>\n",
        "      <td>  46</td>\n",
        "      <td> 123</td>\n",
        "      <td> 0.272189</td>\n",
        "      <td> 0.727811</td>\n",
        "      <td> 169</td>\n",
        "      <td> 124.763023</td>\n",
        "      <td> -91.795275</td>\n",
        "      <td>  44.236977</td>\n",
        "      <td> 251.565340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___stdinp</th>\n",
        "      <td>   0</td>\n",
        "      <td>  93</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>  93</td>\n",
        "      <td>  68.656575</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  24.343425</td>\n",
        "      <td> 249.302811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_fwrite</th>\n",
        "      <td>  55</td>\n",
        "      <td> 116</td>\n",
        "      <td> 0.321637</td>\n",
        "      <td> 0.678363</td>\n",
        "      <td> 171</td>\n",
        "      <td> 126.239509</td>\n",
        "      <td> -91.393256</td>\n",
        "      <td>  44.760491</td>\n",
        "      <td> 220.925324</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_fputc</th>\n",
        "      <td>  32</td>\n",
        "      <td> 101</td>\n",
        "      <td> 0.240602</td>\n",
        "      <td> 0.759398</td>\n",
        "      <td> 133</td>\n",
        "      <td>  98.186284</td>\n",
        "      <td> -71.752361</td>\n",
        "      <td>  34.813716</td>\n",
        "      <td> 215.152034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___error</th>\n",
        "      <td>  85</td>\n",
        "      <td> 127</td>\n",
        "      <td> 0.400943</td>\n",
        "      <td> 0.599057</td>\n",
        "      <td> 212</td>\n",
        "      <td> 156.507461</td>\n",
        "      <td>-103.776912</td>\n",
        "      <td>  55.492539</td>\n",
        "      <td> 210.296380</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___snprintf_chk</th>\n",
        "      <td>   0</td>\n",
        "      <td>  78</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>  78</td>\n",
        "      <td>  57.582934</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  20.417066</td>\n",
        "      <td> 209.092680</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_exit</th>\n",
        "      <td> 230</td>\n",
        "      <td> 189</td>\n",
        "      <td> 0.548926</td>\n",
        "      <td> 0.451074</td>\n",
        "      <td> 419</td>\n",
        "      <td> 309.323708</td>\n",
        "      <td>-136.302148</td>\n",
        "      <td> 109.676292</td>\n",
        "      <td> 205.712812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_puts</th>\n",
        "      <td>  27</td>\n",
        "      <td>  89</td>\n",
        "      <td> 0.232759</td>\n",
        "      <td> 0.767241</td>\n",
        "      <td> 116</td>\n",
        "      <td>  85.636158</td>\n",
        "      <td> -62.330620</td>\n",
        "      <td>  30.363842</td>\n",
        "      <td> 191.418329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_optind</th>\n",
        "      <td>   5</td>\n",
        "      <td>  74</td>\n",
        "      <td> 0.063291</td>\n",
        "      <td> 0.936709</td>\n",
        "      <td>  79</td>\n",
        "      <td>  58.321176</td>\n",
        "      <td> -24.565273</td>\n",
        "      <td>  20.678824</td>\n",
        "      <td> 188.693330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_strncmp</th>\n",
        "      <td>  28</td>\n",
        "      <td>  88</td>\n",
        "      <td> 0.241379</td>\n",
        "      <td> 0.758621</td>\n",
        "      <td> 116</td>\n",
        "      <td>  85.636158</td>\n",
        "      <td> -62.602573</td>\n",
        "      <td>  30.363842</td>\n",
        "      <td> 187.278840</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_putchar</th>\n",
        "      <td>   6</td>\n",
        "      <td>  74</td>\n",
        "      <td> 0.075000</td>\n",
        "      <td> 0.925000</td>\n",
        "      <td>  80</td>\n",
        "      <td>  59.059419</td>\n",
        "      <td> -27.441415</td>\n",
        "      <td>  20.940581</td>\n",
        "      <td> 186.831670</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_optarg</th>\n",
        "      <td>   1</td>\n",
        "      <td>  69</td>\n",
        "      <td> 0.014286</td>\n",
        "      <td> 0.985714</td>\n",
        "      <td>  70</td>\n",
        "      <td>  51.676992</td>\n",
        "      <td>  -7.890025</td>\n",
        "      <td>  18.323008</td>\n",
        "      <td> 182.980956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_strtol</th>\n",
        "      <td>   8</td>\n",
        "      <td>  73</td>\n",
        "      <td> 0.098765</td>\n",
        "      <td> 0.901235</td>\n",
        "      <td>  81</td>\n",
        "      <td>  59.797662</td>\n",
        "      <td> -32.184400</td>\n",
        "      <td>  21.202338</td>\n",
        "      <td> 180.506805</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_stat$INODE64</th>\n",
        "      <td>  21</td>\n",
        "      <td>  80</td>\n",
        "      <td> 0.207921</td>\n",
        "      <td> 0.792079</td>\n",
        "      <td> 101</td>\n",
        "      <td>  74.562517</td>\n",
        "      <td> -53.218850</td>\n",
        "      <td>  26.437483</td>\n",
        "      <td> 177.159010</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_strlen</th>\n",
        "      <td> 171</td>\n",
        "      <td> 150</td>\n",
        "      <td> 0.532710</td>\n",
        "      <td> 0.467290</td>\n",
        "      <td> 321</td>\n",
        "      <td> 236.975920</td>\n",
        "      <td>-111.592881</td>\n",
        "      <td>  84.024080</td>\n",
        "      <td> 173.859559</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___maskrune</th>\n",
        "      <td>  15</td>\n",
        "      <td>  73</td>\n",
        "      <td> 0.170455</td>\n",
        "      <td> 0.829545</td>\n",
        "      <td>  88</td>\n",
        "      <td>  64.965361</td>\n",
        "      <td> -43.974121</td>\n",
        "      <td>  23.034639</td>\n",
        "      <td> 168.405207</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>25 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "                     bad  good    bad_cd   good_cd  total_cd     bad_exp  \\\n",
        "dyld_stub_binder     173   276  0.385301  0.614699       449  331.470990   \n",
        "radr://5614542       107   236  0.311953  0.688047       343  253.217260   \n",
        "___stderrp            35   187  0.157658  0.842342       222  163.889888   \n",
        "_strcmp               51   169  0.231818  0.768182       220  162.413403   \n",
        "___stack_chk_fail     92   190  0.326241  0.673759       282  208.184453   \n",
        "___stack_chk_guard    92   190  0.326241  0.673759       282  208.184453   \n",
        "_fprintf              39   156  0.200000  0.800000       195  143.957334   \n",
        "___stdoutp             8   120  0.062500  0.937500       128   94.495071   \n",
        "__mh_execute_header  218   225  0.492099  0.507901       443  327.041534   \n",
        "_printf               46   123  0.272189  0.727811       169  124.763023   \n",
        "___stdinp              0    93  0.000000  1.000000        93   68.656575   \n",
        "_fwrite               55   116  0.321637  0.678363       171  126.239509   \n",
        "_fputc                32   101  0.240602  0.759398       133   98.186284   \n",
        "___error              85   127  0.400943  0.599057       212  156.507461   \n",
        "___snprintf_chk        0    78  0.000000  1.000000        78   57.582934   \n",
        "_exit                230   189  0.548926  0.451074       419  309.323708   \n",
        "_puts                 27    89  0.232759  0.767241       116   85.636158   \n",
        "_optind                5    74  0.063291  0.936709        79   58.321176   \n",
        "_strncmp              28    88  0.241379  0.758621       116   85.636158   \n",
        "_putchar               6    74  0.075000  0.925000        80   59.059419   \n",
        "_optarg                1    69  0.014286  0.985714        70   51.676992   \n",
        "_strtol                8    73  0.098765  0.901235        81   59.797662   \n",
        "_stat$INODE64         21    80  0.207921  0.792079       101   74.562517   \n",
        "_strlen              171   150  0.532710  0.467290       321  236.975920   \n",
        "___maskrune           15    73  0.170455  0.829545        88   64.965361   \n",
        "\n",
        "                          bad_g    good_exp      good_g  \n",
        "dyld_stub_binder    -224.986050  117.529010  471.251050  \n",
        "radr://5614542      -184.343670   89.782740  456.159232  \n",
        "___stderrp          -108.069271   58.110112  437.119579  \n",
        "_strcmp             -118.148571   57.586597  363.893795  \n",
        "___stack_chk_fail   -150.261006   73.815547  359.272784  \n",
        "___stack_chk_guard  -150.261006   73.815547  359.272784  \n",
        "_fprintf            -101.864515   51.042666  348.564572  \n",
        "___stdoutp           -39.505698   33.504929  306.191801  \n",
        "__mh_execute_header -176.838162  115.958466  298.290746  \n",
        "_printf              -91.795275   44.236977  251.565340  \n",
        "___stdinp              0.000000   24.343425  249.302811  \n",
        "_fwrite              -91.393256   44.760491  220.925324  \n",
        "_fputc               -71.752361   34.813716  215.152034  \n",
        "___error            -103.776912   55.492539  210.296380  \n",
        "___snprintf_chk        0.000000   20.417066  209.092680  \n",
        "_exit               -136.302148  109.676292  205.712812  \n",
        "_puts                -62.330620   30.363842  191.418329  \n",
        "_optind              -24.565273   20.678824  188.693330  \n",
        "_strncmp             -62.602573   30.363842  187.278840  \n",
        "_putchar             -27.441415   20.940581  186.831670  \n",
        "_optarg               -7.890025   18.323008  182.980956  \n",
        "_strtol              -32.184400   21.202338  180.506805  \n",
        "_stat$INODE64        -53.218850   26.437483  177.159010  \n",
        "_strlen             -111.592881   84.024080  173.859559  \n",
        "___maskrune          -43.974121   23.034639  168.405207  \n",
        "\n",
        "[25 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def tokenize_string(string):\n",
      "    if '___cxx_global_var_init' in string:\n",
      "        return '___cxx_global_var_init_TOKEN'\n",
      "    elif '__ZN12_GLOBAL' in string:\n",
      "        return '__ZN12_GLOBAL_TOKEN'\n",
      "    else:\n",
      "        return string"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd_symbols['tokenized string'] = pd_symbols['symbol string'].map(lambda x: tokenize_string(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_ct, df_cd, df_symbol_stats = g_test.highest_gtest_scores(pd_symbols['tokenized string'],\n",
      "                                                     pd_symbols['label'], N=10, matches=5, min_volume=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_symbol_stats.dropna(inplace=True)\n",
      "df_symbol_stats.sort('bad_g', ascending=0).head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bad</th>\n",
        "      <th>good</th>\n",
        "      <th>bad_cd</th>\n",
        "      <th>good_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
        "      <th>bad_g</th>\n",
        "      <th>good_exp</th>\n",
        "      <th>good_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>L\\x89\\xe7\\xff\\x158`\\x02</th>\n",
        "      <td> 4836</td>\n",
        "      <td>   0</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 4836</td>\n",
        "      <td> 4428.513498</td>\n",
        "      <td> 851.367726</td>\n",
        "      <td> 407.486502</td>\n",
        "      <td>    0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>H1\\x8d5A`\\x02</th>\n",
        "      <td> 4836</td>\n",
        "      <td>   0</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 4836</td>\n",
        "      <td> 4428.513498</td>\n",
        "      <td> 851.367726</td>\n",
        "      <td> 407.486502</td>\n",
        "      <td>    0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>___cxx_global_var_init_TOKEN</th>\n",
        "      <td> 1560</td>\n",
        "      <td>   0</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1560</td>\n",
        "      <td> 1428.552741</td>\n",
        "      <td> 274.634750</td>\n",
        "      <td> 131.447259</td>\n",
        "      <td>    0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__ZN12_GLOBAL_TOKEN</th>\n",
        "      <td>  870</td>\n",
        "      <td>   5</td>\n",
        "      <td> 0.994286</td>\n",
        "      <td> 0.005714</td>\n",
        "      <td>  875</td>\n",
        "      <td>  801.271570</td>\n",
        "      <td> 143.190314</td>\n",
        "      <td>  73.728430</td>\n",
        "      <td>  -26.909506</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_malloc</th>\n",
        "      <td>  160</td>\n",
        "      <td> 130</td>\n",
        "      <td> 0.551724</td>\n",
        "      <td> 0.448276</td>\n",
        "      <td>  290</td>\n",
        "      <td>  265.564292</td>\n",
        "      <td>-162.138608</td>\n",
        "      <td>  24.435708</td>\n",
        "      <td>  434.587122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_strlen</th>\n",
        "      <td>  171</td>\n",
        "      <td> 150</td>\n",
        "      <td> 0.532710</td>\n",
        "      <td> 0.467290</td>\n",
        "      <td>  321</td>\n",
        "      <td>  293.952199</td>\n",
        "      <td>-185.279734</td>\n",
        "      <td>  27.047801</td>\n",
        "      <td>  513.908873</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>radr://5614542</th>\n",
        "      <td>  107</td>\n",
        "      <td> 236</td>\n",
        "      <td> 0.311953</td>\n",
        "      <td> 0.688047</td>\n",
        "      <td>  343</td>\n",
        "      <td>  314.098455</td>\n",
        "      <td>-230.451818</td>\n",
        "      <td>  28.901545</td>\n",
        "      <td>  991.170152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>_exit</th>\n",
        "      <td>  230</td>\n",
        "      <td> 189</td>\n",
        "      <td> 0.548926</td>\n",
        "      <td> 0.451074</td>\n",
        "      <td>  419</td>\n",
        "      <td>  383.694615</td>\n",
        "      <td>-235.413120</td>\n",
        "      <td>  35.305385</td>\n",
        "      <td>  634.174947</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__mh_execute_header</th>\n",
        "      <td>  218</td>\n",
        "      <td> 225</td>\n",
        "      <td> 0.492099</td>\n",
        "      <td> 0.507901</td>\n",
        "      <td>  443</td>\n",
        "      <td>  405.672349</td>\n",
        "      <td>-270.778127</td>\n",
        "      <td>  37.327651</td>\n",
        "      <td>  808.364717</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dyld_stub_binder</th>\n",
        "      <td>  173</td>\n",
        "      <td> 276</td>\n",
        "      <td> 0.385301</td>\n",
        "      <td> 0.614699</td>\n",
        "      <td>  449</td>\n",
        "      <td>  411.166783</td>\n",
        "      <td>-299.534738</td>\n",
        "      <td>  37.833217</td>\n",
        "      <td> 1096.941787</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "                               bad  good    bad_cd   good_cd  total_cd  \\\n",
        "L\\x89\\xe7\\xff\\x158`\\x02       4836     0  1.000000  0.000000      4836   \n",
        "H1\\x8d5A`\\x02                 4836     0  1.000000  0.000000      4836   \n",
        "___cxx_global_var_init_TOKEN  1560     0  1.000000  0.000000      1560   \n",
        "__ZN12_GLOBAL_TOKEN            870     5  0.994286  0.005714       875   \n",
        "_malloc                        160   130  0.551724  0.448276       290   \n",
        "_strlen                        171   150  0.532710  0.467290       321   \n",
        "radr://5614542                 107   236  0.311953  0.688047       343   \n",
        "_exit                          230   189  0.548926  0.451074       419   \n",
        "__mh_execute_header            218   225  0.492099  0.507901       443   \n",
        "dyld_stub_binder               173   276  0.385301  0.614699       449   \n",
        "\n",
        "                                  bad_exp       bad_g    good_exp       good_g  \n",
        "L\\x89\\xe7\\xff\\x158`\\x02       4428.513498  851.367726  407.486502     0.000000  \n",
        "H1\\x8d5A`\\x02                 4428.513498  851.367726  407.486502     0.000000  \n",
        "___cxx_global_var_init_TOKEN  1428.552741  274.634750  131.447259     0.000000  \n",
        "__ZN12_GLOBAL_TOKEN            801.271570  143.190314   73.728430   -26.909506  \n",
        "_malloc                        265.564292 -162.138608   24.435708   434.587122  \n",
        "_strlen                        293.952199 -185.279734   27.047801   513.908873  \n",
        "radr://5614542                 314.098455 -230.451818   28.901545   991.170152  \n",
        "_exit                          383.694615 -235.413120   35.305385   634.174947  \n",
        "__mh_execute_header            405.672349 -270.778127   37.327651   808.364717  \n",
        "dyld_stub_binder               411.166783 -299.534738   37.833217  1096.941787  \n",
        "\n",
        "[10 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def g_aggregate(df_stats, sequence, name):\n",
      "    try:\n",
      "        g_scores = [df_stats.ix[tokenize_string(item)][name] for item in sequence]\n",
      "    except KeyError:\n",
      "        return 0\n",
      "    return sum(g_scores)/len(g_scores) if g_scores else 0 # Average"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_all['symbol table strings malicious_g'] = \\\n",
      "    df_all['symbol table strings'].map(lambda x: g_aggregate(df_symbol_stats, x, 'bad_g'))\n",
      "df_all['symbol table strings clean_g'] = \\\n",
      "    df_all['symbol table strings'].map(lambda x: g_aggregate(df_symbol_stats, x, 'good_g'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "segment_names = []\n",
      "for strings, label in zip(df_all['segment names'], df_all['label']):\n",
      "    for name in strings:\n",
      "        segment_names.append({'segment names': name, 'label': label})\n",
      "\n",
      "pd_segment_names = pd.DataFrame.from_records(segment_names)\n",
      "pd_segment_names.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>label</th>\n",
        "      <th>segment names</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> bad</td>\n",
        "      <td> __PAGEZERO</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> bad</td>\n",
        "      <td>     __TEXT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> bad</td>\n",
        "      <td>     __DATA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> bad</td>\n",
        "      <td> __LINKEDIT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> bad</td>\n",
        "      <td>     __TEXT</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "  label segment names\n",
        "0   bad    __PAGEZERO\n",
        "1   bad        __TEXT\n",
        "2   bad        __DATA\n",
        "3   bad    __LINKEDIT\n",
        "4   bad        __TEXT\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Spin up our g_test class\n",
      "g_test = ss.GTest()\n",
      "\n",
      "df_ct, df_cd, df_segment_stats = g_test.highest_gtest_scores(pd_segment_names['segment names'],\n",
      "                                                     pd_segment_names['label'], N=0, matches=0, min_volume=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_segment_stats.dropna(inplace=True)\n",
      "df_segment_stats.sort('bad_g', ascending=0).head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bad</th>\n",
        "      <th>good</th>\n",
        "      <th>bad_cd</th>\n",
        "      <th>good_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
        "      <th>bad_g</th>\n",
        "      <th>good_exp</th>\n",
        "      <th>good_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>__OBJC</th>\n",
        "      <td>  55</td>\n",
        "      <td>  17</td>\n",
        "      <td> 0.763889</td>\n",
        "      <td> 0.236111</td>\n",
        "      <td>  72</td>\n",
        "      <td>  34.263158</td>\n",
        "      <td> 52.058877</td>\n",
        "      <td>  37.736842</td>\n",
        "      <td>-27.112400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__IMPORT</th>\n",
        "      <td>  48</td>\n",
        "      <td>  28</td>\n",
        "      <td> 0.631579</td>\n",
        "      <td> 0.368421</td>\n",
        "      <td>  76</td>\n",
        "      <td>  36.166667</td>\n",
        "      <td> 27.174060</td>\n",
        "      <td>  39.833333</td>\n",
        "      <td>-19.739976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__PAGEZERO</th>\n",
        "      <td> 218</td>\n",
        "      <td> 225</td>\n",
        "      <td> 0.492099</td>\n",
        "      <td> 0.507901</td>\n",
        "      <td> 443</td>\n",
        "      <td> 210.813596</td>\n",
        "      <td> 14.615046</td>\n",
        "      <td> 232.186404</td>\n",
        "      <td>-14.148050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   3</td>\n",
        "      <td>   0</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   3</td>\n",
        "      <td>   1.427632</td>\n",
        "      <td>  4.455573</td>\n",
        "      <td>   1.572368</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__INIT_STUB</th>\n",
        "      <td>   2</td>\n",
        "      <td>   0</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   2</td>\n",
        "      <td>   0.951754</td>\n",
        "      <td>  2.970382</td>\n",
        "      <td>   1.048246</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>.ida</th>\n",
        "      <td>   0</td>\n",
        "      <td>   4</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>   4</td>\n",
        "      <td>   1.903509</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>   2.096491</td>\n",
        "      <td>  5.168234</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__LINKEDIT</th>\n",
        "      <td> 253</td>\n",
        "      <td> 305</td>\n",
        "      <td> 0.453405</td>\n",
        "      <td> 0.546595</td>\n",
        "      <td> 558</td>\n",
        "      <td> 265.539474</td>\n",
        "      <td>-24.477253</td>\n",
        "      <td> 292.460526</td>\n",
        "      <td> 25.609064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__TEXT</th>\n",
        "      <td> 253</td>\n",
        "      <td> 305</td>\n",
        "      <td> 0.453405</td>\n",
        "      <td> 0.546595</td>\n",
        "      <td> 558</td>\n",
        "      <td> 265.539474</td>\n",
        "      <td>-24.477253</td>\n",
        "      <td> 292.460526</td>\n",
        "      <td> 25.609064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__DATA</th>\n",
        "      <td> 253</td>\n",
        "      <td> 311</td>\n",
        "      <td> 0.448582</td>\n",
        "      <td> 0.551418</td>\n",
        "      <td> 564</td>\n",
        "      <td> 268.394737</td>\n",
        "      <td>-29.889069</td>\n",
        "      <td> 295.605263</td>\n",
        "      <td> 31.577645</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "             bad  good    bad_cd   good_cd  total_cd     bad_exp      bad_g  \\\n",
        "__OBJC        55    17  0.763889  0.236111        72   34.263158  52.058877   \n",
        "__IMPORT      48    28  0.631579  0.368421        76   36.166667  27.174060   \n",
        "__PAGEZERO   218   225  0.492099  0.507901       443  210.813596  14.615046   \n",
        "               3     0  1.000000  0.000000         3    1.427632   4.455573   \n",
        "__INIT_STUB    2     0  1.000000  0.000000         2    0.951754   2.970382   \n",
        ".ida           0     4  0.000000  1.000000         4    1.903509   0.000000   \n",
        "__LINKEDIT   253   305  0.453405  0.546595       558  265.539474 -24.477253   \n",
        "__TEXT       253   305  0.453405  0.546595       558  265.539474 -24.477253   \n",
        "__DATA       253   311  0.448582  0.551418       564  268.394737 -29.889069   \n",
        "\n",
        "               good_exp     good_g  \n",
        "__OBJC        37.736842 -27.112400  \n",
        "__IMPORT      39.833333 -19.739976  \n",
        "__PAGEZERO   232.186404 -14.148050  \n",
        "               1.572368   0.000000  \n",
        "__INIT_STUB    1.048246   0.000000  \n",
        ".ida           2.096491   5.168234  \n",
        "__LINKEDIT   292.460526  25.609064  \n",
        "__TEXT       292.460526  25.609064  \n",
        "__DATA       295.605263  31.577645  \n",
        "\n",
        "[9 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = []\n",
      "for strings, label in zip(df_all['section names'], df_all['label']):\n",
      "    for name in strings:\n",
      "        s.append({'section names': name, 'label': label})\n",
      "\n",
      "pd_section_names = pd.DataFrame.from_records(s)\n",
      "print pd_section_names.shape\n",
      "pd_section_names.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(8289, 2)\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>label</th>\n",
        "      <th>section names</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> bad</td>\n",
        "      <td>         __text</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> bad</td>\n",
        "      <td> __symbol_stub1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> bad</td>\n",
        "      <td>  __stub_helper</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> bad</td>\n",
        "      <td>      __cstring</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> bad</td>\n",
        "      <td>  __unwind_info</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "  label   section names\n",
        "0   bad          __text\n",
        "1   bad  __symbol_stub1\n",
        "2   bad   __stub_helper\n",
        "3   bad       __cstring\n",
        "4   bad   __unwind_info\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Spin up our g_test class\n",
      "g_test = ss.GTest()\n",
      "\n",
      "# Here we'd like to see how various exploits (description) are related to\n",
      "# the ASN (Autonomous System Number) associated with the ip/domain.\n",
      "df_ct, df_cd, df_section_stats = g_test.highest_gtest_scores(pd_section_names['section names'],\n",
      "                                                     pd_section_names['label'], N=0, matches=0, min_volume=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_section_stats.dropna(inplace=True)\n",
      "df_section_stats.sort('bad_g', ascending=0).head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bad</th>\n",
        "      <th>good</th>\n",
        "      <th>bad_cd</th>\n",
        "      <th>good_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
        "      <th>bad_g</th>\n",
        "      <th>good_exp</th>\n",
        "      <th>good_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>__dyld</th>\n",
        "      <td> 192</td>\n",
        "      <td>  53</td>\n",
        "      <td> 0.783673</td>\n",
        "      <td> 0.216327</td>\n",
        "      <td> 245</td>\n",
        "      <td> 125.706961</td>\n",
        "      <td> 162.640082</td>\n",
        "      <td> 119.293039</td>\n",
        "      <td>-85.996853</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__symbol_stub1</th>\n",
        "      <td>  55</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.982143</td>\n",
        "      <td> 0.017857</td>\n",
        "      <td>  56</td>\n",
        "      <td>  28.733020</td>\n",
        "      <td>  71.421483</td>\n",
        "      <td>  27.266980</td>\n",
        "      <td> -6.611353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__symbol_stub</th>\n",
        "      <td> 124</td>\n",
        "      <td>  62</td>\n",
        "      <td> 0.666667</td>\n",
        "      <td> 0.333333</td>\n",
        "      <td> 186</td>\n",
        "      <td>  95.434672</td>\n",
        "      <td>  64.936223</td>\n",
        "      <td>  90.565328</td>\n",
        "      <td>-46.988195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__common</th>\n",
        "      <td> 212</td>\n",
        "      <td> 156</td>\n",
        "      <td> 0.576087</td>\n",
        "      <td> 0.423913</td>\n",
        "      <td> 368</td>\n",
        "      <td> 188.816986</td>\n",
        "      <td>  49.102615</td>\n",
        "      <td> 179.183014</td>\n",
        "      <td>-43.228130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__instance_vars</th>\n",
        "      <td>  43</td>\n",
        "      <td>   6</td>\n",
        "      <td> 0.877551</td>\n",
        "      <td> 0.122449</td>\n",
        "      <td>  49</td>\n",
        "      <td>  25.141392</td>\n",
        "      <td>  46.154870</td>\n",
        "      <td>  23.858608</td>\n",
        "      <td>-16.564627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__cls_refs</th>\n",
        "      <td>  50</td>\n",
        "      <td>  12</td>\n",
        "      <td> 0.806452</td>\n",
        "      <td> 0.193548</td>\n",
        "      <td>  62</td>\n",
        "      <td>  31.811557</td>\n",
        "      <td>  45.219334</td>\n",
        "      <td>  30.188443</td>\n",
        "      <td>-22.141260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__message_refs</th>\n",
        "      <td>  50</td>\n",
        "      <td>  12</td>\n",
        "      <td> 0.806452</td>\n",
        "      <td> 0.193548</td>\n",
        "      <td>  62</td>\n",
        "      <td>  31.811557</td>\n",
        "      <td>  45.219334</td>\n",
        "      <td>  30.188443</td>\n",
        "      <td>-22.141260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__module_info</th>\n",
        "      <td>  51</td>\n",
        "      <td>  13</td>\n",
        "      <td> 0.796875</td>\n",
        "      <td> 0.203125</td>\n",
        "      <td>  64</td>\n",
        "      <td>  32.837737</td>\n",
        "      <td>  44.905221</td>\n",
        "      <td>  31.162263</td>\n",
        "      <td>-22.730721</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__meta_class</th>\n",
        "      <td>  44</td>\n",
        "      <td>   8</td>\n",
        "      <td> 0.846154</td>\n",
        "      <td> 0.153846</td>\n",
        "      <td>  52</td>\n",
        "      <td>  26.680661</td>\n",
        "      <td>  44.022056</td>\n",
        "      <td>  25.319339</td>\n",
        "      <td>-18.434031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__symbols</th>\n",
        "      <td>  44</td>\n",
        "      <td>   8</td>\n",
        "      <td> 0.846154</td>\n",
        "      <td> 0.153846</td>\n",
        "      <td>  52</td>\n",
        "      <td>  26.680661</td>\n",
        "      <td>  44.022056</td>\n",
        "      <td>  25.319339</td>\n",
        "      <td>-18.434031</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "                 bad  good    bad_cd   good_cd  total_cd     bad_exp  \\\n",
        "__dyld           192    53  0.783673  0.216327       245  125.706961   \n",
        "__symbol_stub1    55     1  0.982143  0.017857        56   28.733020   \n",
        "__symbol_stub    124    62  0.666667  0.333333       186   95.434672   \n",
        "__common         212   156  0.576087  0.423913       368  188.816986   \n",
        "__instance_vars   43     6  0.877551  0.122449        49   25.141392   \n",
        "__cls_refs        50    12  0.806452  0.193548        62   31.811557   \n",
        "__message_refs    50    12  0.806452  0.193548        62   31.811557   \n",
        "__module_info     51    13  0.796875  0.203125        64   32.837737   \n",
        "__meta_class      44     8  0.846154  0.153846        52   26.680661   \n",
        "__symbols         44     8  0.846154  0.153846        52   26.680661   \n",
        "\n",
        "                      bad_g    good_exp     good_g  \n",
        "__dyld           162.640082  119.293039 -85.996853  \n",
        "__symbol_stub1    71.421483   27.266980  -6.611353  \n",
        "__symbol_stub     64.936223   90.565328 -46.988195  \n",
        "__common          49.102615  179.183014 -43.228130  \n",
        "__instance_vars   46.154870   23.858608 -16.564627  \n",
        "__cls_refs        45.219334   30.188443 -22.141260  \n",
        "__message_refs    45.219334   30.188443 -22.141260  \n",
        "__module_info     44.905221   31.162263 -22.730721  \n",
        "__meta_class      44.022056   25.319339 -18.434031  \n",
        "__symbols         44.022056   25.319339 -18.434031  \n",
        "\n",
        "[10 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_section_stats.sort('good_g', ascending=0).head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bad</th>\n",
        "      <th>good</th>\n",
        "      <th>bad_cd</th>\n",
        "      <th>good_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
        "      <th>bad_g</th>\n",
        "      <th>good_exp</th>\n",
        "      <th>good_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>__stubs</th>\n",
        "      <td>  28</td>\n",
        "      <td> 210</td>\n",
        "      <td> 0.117647</td>\n",
        "      <td> 0.882353</td>\n",
        "      <td> 238</td>\n",
        "      <td> 122.115334</td>\n",
        "      <td> -82.474641</td>\n",
        "      <td> 115.884666</td>\n",
        "      <td> 249.695077</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__got</th>\n",
        "      <td>  29</td>\n",
        "      <td> 171</td>\n",
        "      <td> 0.145000</td>\n",
        "      <td> 0.855000</td>\n",
        "      <td> 200</td>\n",
        "      <td> 102.617927</td>\n",
        "      <td> -73.295575</td>\n",
        "      <td>  97.382073</td>\n",
        "      <td> 192.553326</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__unwind_info</th>\n",
        "      <td> 192</td>\n",
        "      <td> 301</td>\n",
        "      <td> 0.389452</td>\n",
        "      <td> 0.610548</td>\n",
        "      <td> 493</td>\n",
        "      <td> 252.953191</td>\n",
        "      <td>-105.872288</td>\n",
        "      <td> 240.046809</td>\n",
        "      <td> 136.218346</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__stub_helper</th>\n",
        "      <td> 192</td>\n",
        "      <td> 273</td>\n",
        "      <td> 0.412903</td>\n",
        "      <td> 0.587097</td>\n",
        "      <td> 465</td>\n",
        "      <td> 238.586681</td>\n",
        "      <td> -83.419129</td>\n",
        "      <td> 226.413319</td>\n",
        "      <td> 102.161853</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__nl_symbol_ptr</th>\n",
        "      <td> 198</td>\n",
        "      <td> 274</td>\n",
        "      <td> 0.419492</td>\n",
        "      <td> 0.580508</td>\n",
        "      <td> 472</td>\n",
        "      <td> 242.178309</td>\n",
        "      <td> -79.757266</td>\n",
        "      <td> 229.821691</td>\n",
        "      <td>  96.351745</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__const</th>\n",
        "      <td> 272</td>\n",
        "      <td> 341</td>\n",
        "      <td> 0.443719</td>\n",
        "      <td> 0.556281</td>\n",
        "      <td> 613</td>\n",
        "      <td> 314.523947</td>\n",
        "      <td> -79.020434</td>\n",
        "      <td> 298.476053</td>\n",
        "      <td>  90.837472</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__la_symbol_ptr</th>\n",
        "      <td> 204</td>\n",
        "      <td> 273</td>\n",
        "      <td> 0.427673</td>\n",
        "      <td> 0.572327</td>\n",
        "      <td> 477</td>\n",
        "      <td> 244.743757</td>\n",
        "      <td> -74.293446</td>\n",
        "      <td> 232.256243</td>\n",
        "      <td>  88.250273</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__cstring</th>\n",
        "      <td> 242</td>\n",
        "      <td> 302</td>\n",
        "      <td> 0.444853</td>\n",
        "      <td> 0.555147</td>\n",
        "      <td> 544</td>\n",
        "      <td> 279.120762</td>\n",
        "      <td> -69.070092</td>\n",
        "      <td> 264.879238</td>\n",
        "      <td>  79.216414</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__eh_frame</th>\n",
        "      <td> 126</td>\n",
        "      <td> 186</td>\n",
        "      <td> 0.403846</td>\n",
        "      <td> 0.596154</td>\n",
        "      <td> 312</td>\n",
        "      <td> 160.083967</td>\n",
        "      <td> -60.332974</td>\n",
        "      <td> 151.916033</td>\n",
        "      <td>  75.299763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>__text</th>\n",
        "      <td> 255</td>\n",
        "      <td> 304</td>\n",
        "      <td> 0.456172</td>\n",
        "      <td> 0.543828</td>\n",
        "      <td> 559</td>\n",
        "      <td> 286.817107</td>\n",
        "      <td> -59.966417</td>\n",
        "      <td> 272.182893</td>\n",
        "      <td>  67.216504</td>\n",
        "    </tr>\n",
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        "</table>\n",
        "<p>10 rows \u00d7 9 columns</p>\n",
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "                 bad  good    bad_cd   good_cd  total_cd     bad_exp  \\\n",
        "__stubs           28   210  0.117647  0.882353       238  122.115334   \n",
        "__got             29   171  0.145000  0.855000       200  102.617927   \n",
        "__unwind_info    192   301  0.389452  0.610548       493  252.953191   \n",
        "__stub_helper    192   273  0.412903  0.587097       465  238.586681   \n",
        "__nl_symbol_ptr  198   274  0.419492  0.580508       472  242.178309   \n",
        "__const          272   341  0.443719  0.556281       613  314.523947   \n",
        "__la_symbol_ptr  204   273  0.427673  0.572327       477  244.743757   \n",
        "__cstring        242   302  0.444853  0.555147       544  279.120762   \n",
        "__eh_frame       126   186  0.403846  0.596154       312  160.083967   \n",
        "__text           255   304  0.456172  0.543828       559  286.817107   \n",
        "\n",
        "                      bad_g    good_exp      good_g  \n",
        "__stubs          -82.474641  115.884666  249.695077  \n",
        "__got            -73.295575   97.382073  192.553326  \n",
        "__unwind_info   -105.872288  240.046809  136.218346  \n",
        "__stub_helper    -83.419129  226.413319  102.161853  \n",
        "__nl_symbol_ptr  -79.757266  229.821691   96.351745  \n",
        "__const          -79.020434  298.476053   90.837472  \n",
        "__la_symbol_ptr  -74.293446  232.256243   88.250273  \n",
        "__cstring        -69.070092  264.879238   79.216414  \n",
        "__eh_frame       -60.332974  151.916033   75.299763  \n",
        "__text           -59.966417  272.182893   67.216504  \n",
        "\n",
        "[10 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_all['segment names malicious_g'] = df_all['segment names'].map(lambda x: g_aggregate(df_segment_stats, x, 'bad_g'))\n",
      "df_all['segment names clean_g'] = df_all['segment names'].map(lambda x: g_aggregate(df_segment_stats, x, 'good_g'))\n",
      "df_all['section names malicious_g'] = df_all['section names'].map(lambda x: g_aggregate(df_section_stats, x, 'bad_g'))\n",
      "df_all['section names clean_g'] = df_all['section names'].map(lambda x: g_aggregate(df_section_stats, x, 'good_g'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g_test = ss.GTest()\n",
      "df_entrypoint = pd.DataFrame(df_all, columns=['entry point section name', 'entry point segment name',\n",
      "                                              'entry point instruction type', 'label'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_entrypoint = df_entrypoint.replace(0, np.nan)\n",
      "df_entrypoint.dropna(inplace=True)\n",
      "df_entrypoint.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>entry point section name</th>\n",
        "      <th>entry point segment name</th>\n",
        "      <th>entry point instruction type</th>\n",
        "      <th>label</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> __text</td>\n",
        "      <td> __TEXT</td>\n",
        "      <td> push</td>\n",
        "      <td> bad</td>\n",
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        "    <tr>\n",
        "      <th>1</th>\n",
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        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> __text</td>\n",
        "      <td> __TEXT</td>\n",
        "      <td>  pop</td>\n",
        "      <td> bad</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> __text</td>\n",
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        "      <td> push</td>\n",
        "      <td> bad</td>\n",
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        "      <th>4</th>\n",
        "      <td> __text</td>\n",
        "      <td> __TEXT</td>\n",
        "      <td>  add</td>\n",
        "      <td> bad</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "  entry point section name entry point segment name  \\\n",
        "0                   __text                   __TEXT   \n",
        "1                       -1                       -1   \n",
        "2                   __text                   __TEXT   \n",
        "3                   __text                   __TEXT   \n",
        "4                   __text                   __TEXT   \n",
        "\n",
        "  entry point instruction type label  \n",
        "0                         push   bad  \n",
        "1                           -1   bad  \n",
        "2                          pop   bad  \n",
        "3                         push   bad  \n",
        "4                          add   bad  \n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_ct, df_cd, df_ep_section_stats = g_test.highest_gtest_scores(df_entrypoint['entry point section name'],\n",
      "                                                     df_entrypoint['label'], N=0, matches=0, min_volume=5)\n",
      "df_ct, df_cd, df_ep_segment_stats = g_test.highest_gtest_scores(df_entrypoint['entry point segment name'],\n",
      "                                                     df_entrypoint['label'], N=0, matches=0, min_volume=5)\n",
      "df_ct, df_cd, df_ep_instruction_stats = g_test.highest_gtest_scores(df_entrypoint['entry point instruction type'],\n",
      "                                                     df_entrypoint['label'], N=0, matches=0, min_volume=5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_ep_section_stats.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bad</th>\n",
        "      <th>good</th>\n",
        "      <th>bad_cd</th>\n",
        "      <th>good_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
        "      <th>bad_g</th>\n",
        "      <th>good_exp</th>\n",
        "      <th>good_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>__text</th>\n",
        "      <td> 214</td>\n",
        "      <td> 225</td>\n",
        "      <td> 0.487472</td>\n",
        "      <td> 0.512528</td>\n",
        "      <td> 439</td>\n",
        "      <td> 199.901786</td>\n",
        "      <td> 29.168132</td>\n",
        "      <td> 239.098214</td>\n",
        "      <td>-27.348302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-1</th>\n",
        "      <td>  34</td>\n",
        "      <td>  80</td>\n",
        "      <td> 0.298246</td>\n",
        "      <td> 0.701754</td>\n",
        "      <td> 114</td>\n",
        "      <td>  51.910714</td>\n",
        "      <td>-28.775199</td>\n",
        "      <td>  62.089286</td>\n",
        "      <td> 40.552511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>COULD NOT FIND</th>\n",
        "      <td>   7</td>\n",
        "      <td>   0</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   7</td>\n",
        "      <td>   3.187500</td>\n",
        "      <td> 11.013425</td>\n",
        "      <td>   3.812500</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "                bad  good    bad_cd   good_cd  total_cd     bad_exp  \\\n",
        "__text          214   225  0.487472  0.512528       439  199.901786   \n",
        "-1               34    80  0.298246  0.701754       114   51.910714   \n",
        "COULD NOT FIND    7     0  1.000000  0.000000         7    3.187500   \n",
        "\n",
        "                    bad_g    good_exp     good_g  \n",
        "__text          29.168132  239.098214 -27.348302  \n",
        "-1             -28.775199   62.089286  40.552511  \n",
        "COULD NOT FIND  11.013425    3.812500   0.000000  \n",
        "\n",
        "[3 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_ep_segment_stats.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bad</th>\n",
        "      <th>good</th>\n",
        "      <th>bad_cd</th>\n",
        "      <th>good_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
        "      <th>bad_g</th>\n",
        "      <th>good_exp</th>\n",
        "      <th>good_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>__TEXT</th>\n",
        "      <td> 214</td>\n",
        "      <td> 225</td>\n",
        "      <td> 0.487472</td>\n",
        "      <td> 0.512528</td>\n",
        "      <td> 439</td>\n",
        "      <td> 199.901786</td>\n",
        "      <td> 29.168132</td>\n",
        "      <td> 239.098214</td>\n",
        "      <td>-27.348302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-1</th>\n",
        "      <td>  34</td>\n",
        "      <td>  80</td>\n",
        "      <td> 0.298246</td>\n",
        "      <td> 0.701754</td>\n",
        "      <td> 114</td>\n",
        "      <td>  51.910714</td>\n",
        "      <td>-28.775199</td>\n",
        "      <td>  62.089286</td>\n",
        "      <td> 40.552511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>COULD NOT FIND</th>\n",
        "      <td>   7</td>\n",
        "      <td>   0</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   7</td>\n",
        "      <td>   3.187500</td>\n",
        "      <td> 11.013425</td>\n",
        "      <td>   3.812500</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "                bad  good    bad_cd   good_cd  total_cd     bad_exp  \\\n",
        "__TEXT          214   225  0.487472  0.512528       439  199.901786   \n",
        "-1               34    80  0.298246  0.701754       114   51.910714   \n",
        "COULD NOT FIND    7     0  1.000000  0.000000         7    3.187500   \n",
        "\n",
        "                    bad_g    good_exp     good_g  \n",
        "__TEXT          29.168132  239.098214 -27.348302  \n",
        "-1             -28.775199   62.089286  40.552511  \n",
        "COULD NOT FIND  11.013425    3.812500   0.000000  \n",
        "\n",
        "[3 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_ep_instruction_stats.dropna(inplace=True)\n",
      "df_ep_instruction_stats.sort('bad_g', ascending=0).head(20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>bad</th>\n",
        "      <th>good</th>\n",
        "      <th>bad_cd</th>\n",
        "      <th>good_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>bad_exp</th>\n",
        "      <th>bad_g</th>\n",
        "      <th>good_exp</th>\n",
        "      <th>good_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>push</th>\n",
        "      <td> 155</td>\n",
        "      <td> 153</td>\n",
        "      <td> 0.503247</td>\n",
        "      <td> 0.496753</td>\n",
        "      <td> 308</td>\n",
        "      <td> 140.250000</td>\n",
        "      <td> 30.999557</td>\n",
        "      <td> 167.750000</td>\n",
        "      <td>-28.163277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>COULD NOT FIND</th>\n",
        "      <td>   7</td>\n",
        "      <td>   0</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   7</td>\n",
        "      <td>   3.187500</td>\n",
        "      <td> 11.013425</td>\n",
        "      <td>   3.812500</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pop</th>\n",
        "      <td>   5</td>\n",
        "      <td>   1</td>\n",
        "      <td> 0.833333</td>\n",
        "      <td> 0.166667</td>\n",
        "      <td>   6</td>\n",
        "      <td>   2.732143</td>\n",
        "      <td>  6.043517</td>\n",
        "      <td>   3.267857</td>\n",
        "      <td> -2.368269</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>das</th>\n",
        "      <td>   0</td>\n",
        "      <td>   8</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>   8</td>\n",
        "      <td>   3.642857</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>   4.357143</td>\n",
        "      <td>  9.722000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>add</th>\n",
        "      <td>  42</td>\n",
        "      <td>  54</td>\n",
        "      <td> 0.437500</td>\n",
        "      <td> 0.562500</td>\n",
        "      <td>  96</td>\n",
        "      <td>  43.714286</td>\n",
        "      <td> -3.360448</td>\n",
        "      <td>  52.285714</td>\n",
        "      <td>  3.484173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-1</th>\n",
        "      <td>  34</td>\n",
        "      <td>  80</td>\n",
        "      <td> 0.298246</td>\n",
        "      <td> 0.701754</td>\n",
        "      <td> 114</td>\n",
        "      <td>  51.910714</td>\n",
        "      <td>-28.775199</td>\n",
        "      <td>  62.089286</td>\n",
        "      <td> 40.552511</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "                bad  good    bad_cd   good_cd  total_cd     bad_exp  \\\n",
        "push            155   153  0.503247  0.496753       308  140.250000   \n",
        "COULD NOT FIND    7     0  1.000000  0.000000         7    3.187500   \n",
        "pop               5     1  0.833333  0.166667         6    2.732143   \n",
        "das               0     8  0.000000  1.000000         8    3.642857   \n",
        "add              42    54  0.437500  0.562500        96   43.714286   \n",
        "-1               34    80  0.298246  0.701754       114   51.910714   \n",
        "\n",
        "                    bad_g    good_exp     good_g  \n",
        "push            30.999557  167.750000 -28.163277  \n",
        "COULD NOT FIND  11.013425    3.812500   0.000000  \n",
        "pop              6.043517    3.267857  -2.368269  \n",
        "das              0.000000    4.357143   9.722000  \n",
        "add             -3.360448   52.285714   3.484173  \n",
        "-1             -28.775199   62.089286  40.552511  \n",
        "\n",
        "[6 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_entrypoint['entry point instruction type'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x116998210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AR9yeZ+4UnXbRaReddtFpF5120WkXnXbRaRed9rndysAVAAAAABDSTrnGddKk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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x118661850>"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf_final = sklearn.ensemble.RandomForestClassifier(n_estimators=50)\n",
      "final_labels = list(df_all.columns.values)\n",
      "final_labels.remove('label')\n",
      "final_labels.remove('cputype')\n",
      "final_labels.remove('LC_UNIXTHREAD')\n",
      "final_labels.remove('LC_MAIN')\n",
      "final_labels.remove('const strings')\n",
      "final_labels.remove('symbol table strings')\n",
      "final_labels.remove('segment names')\n",
      "final_labels.remove('section names')\n",
      "final_labels.remove('entry point section name')\n",
      "final_labels.remove('entry point segment name')\n",
      "final_labels.remove('entry point instruction type')\n",
      "X_final = df_all.as_matrix(final_labels)\n",
      "y_final = np.array(df_all['label'].tolist())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scores = sklearn.cross_validation.cross_val_score(clf_final, X_final, y_final, cv=10, n_jobs=5)\n",
      "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Accuracy: 0.96 (+/- 0.06)\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 80/20 Split for predictive test\n",
      "X_train_final, X_test_final, y_train_final, y_test_final = train_test_split(X_final, y_final,\n",
      "                                                                            test_size=my_tsize,\n",
      "                                                                            random_state=my_seed)\n",
      "clf_final.fit(X_train_final, y_train_final)\n",
      "y_pred_final = clf_final.predict(X_test_final)\n",
      "cm_final = confusion_matrix(y_test_final, y_pred_final, labels)\n",
      "plot_cm(cm_final, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "good/good: 94.20% (65/69)\n",
        "good/bad: 5.80% (4/69)\n",
        "bad/good: 2.33% (1/43)\n",
        "bad/bad: 97.67% (42/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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goKAApqamwlHQkuTk5CA0NBSJiYmoqanB4MGDsW7dOomzgNXX1+PLL7/EN998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\nkoiISAYDkoiISMb/A6QhhCw6uuxDAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117b52350>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### You can set threshold values to your priorities."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_probs_final = clf_final.predict_proba(X_test_final)[:,0]\n",
      "thres = .8 # This can be set to whatever you'd like\n",
      "y_pred_final[y_probs_final<thres] = 'good'\n",
      "y_pred_final[y_probs_final>=thres] = 'bad'\n",
      "cm = confusion_matrix(y_test_final, y_pred_final, labels)\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "good/good: 98.55% (68/69)\n",
        "good/bad: 1.45% (1/69)\n",
        "bad/good: 16.28% (7/43)\n",
        "bad/bad: 83.72% (36/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JjIzExYsXkZ2djaKiIpibm4tGQUuTl5eH8PBwpKSkoL6+HsOGDcP69eulzgLW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117b2d1d0>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Most important features\n",
      "<font size=4>Now that we added in the g-scores from the strings of section names, segment names, and symbol table, now what are the most important features?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "importances = zip(final_labels, clf_final.feature_importances_)\n",
      "importances.sort(key=lambda k:k[1], reverse=True)\n",
      "for im in importances[0:20]:\n",
      "    print im[0].ljust(35), im[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "section names malicious_g           0.0657407559464\n",
        "LC_SOURCE_VERSION                   0.0553265015965\n",
        "section names clean_g               0.0535257311096\n",
        "LC_DYLIB_CODE_SIGN_DRS              0.048614836495\n",
        "LC_DATA_IN_CODE                     0.0464634185482\n",
        "rebase_size                         0.043812886794\n",
        "LC_CODE_SIGNATURE                   0.0334035056079\n",
        "rebase_off                          0.0311568256564\n",
        "text_section_align                  0.0232383559178\n",
        "data_nsects                         0.021464002581\n",
        "linkedit_vmsize                     0.0198348290427\n",
        "lazy_bind_size                      0.0173961188418\n",
        "pz_vmaddr                           0.0165259506433\n",
        "pz_vmsize                           0.0156923760629\n",
        "nextdefsym                          0.0152103851287\n",
        "pz_fileoff                          0.0141570209658\n",
        "entry point offset                  0.0140948505669\n",
        "nlocrel                             0.0131386093896\n",
        "bind_size                           0.0121543445893\n",
        "pz_flags                            0.0120473460984\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Try a new classifier with only the 20 most important features"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf_most = sklearn.ensemble.RandomForestClassifier(n_estimators=50)\n",
      "X = df_all.as_matrix([item[0] for item in importances[:20]])\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=my_tsize, random_state=my_seed)\n",
      "clf_most.fit(X_train, y_train)\n",
      "y_pred = clf_most.predict(X_test)\n",
      "cm = confusion_matrix(y_test, y_pred, labels)\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "good/good: 92.75% (64/69)\n",
        "good/bad: 7.25% (5/69)\n",
        "bad/good: 2.33% (1/43)\n",
        "bad/bad: 97.67% (42/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lS8jKykJRURFMTU2Fo6AlycvLQ1hYGJKTk1FfX48RI0Zg/fr1EmcBa2pqwhdf\nfIGvv/4at27dgpGREQICArBhwwaRS5AkoXOQr6mnT5+KlUVGRgIA3njjjVddHULIa2z16tVISkrC\nwIEDoaen1+IYhIKCAri6uuLChQvCmbuqqqowYcIEJCQkiMUvXboUy5Ytw9ChQ7F9+3ZMnToVX375\nJXx8fFq93ppakK8pDw8P4aCZpqYmJCQkIC4uDqNHj0ZycjINkiGEvDKCeaABYOjQoaipqZF4T04A\nCAgIwLFjx5CVlQU7OzsAQHV1NYYMGQJVVVXk5uYKY69duwZbW1v4+/uL3Dx6+/btWLx4MQ4ePCg2\nYcqLqAX5mvLx8cHly5exZs0ahIWF4caNG1i+fDlOnTpFyZEQ8koJkmNrqqurceLECbi7uwuTIwBo\naGggODgY+fn5yMjIEJYLRuovWbJEZDvz58+Hurq6xFvGvYhGsb6mQkNDERoa2tHVIIQQqeXk5KC+\nvh6jRo0SW+bi4gIAyMzMhJOTEwAgIyMDSkpKcHZ2Fonl8/kYNmyYSDKVhFqQhBBCuoSSkhIAgLGx\nsdgyQVlxcbFIvKGhIZSVlSXGP3z4UOSa7ZdRgiSEENIl1NTUAHjeAnyZqqqqSIzg/5Jim4t/GSVI\nQgghXYLgsoy6ujqxZbW1tSIxgv9LihXEcxzX4qUedA5SRg662rj0+ElHV4MQ0s0Y9HXCw+KLrQd2\nIlqcEqrQ1HrgSzQ1NfHkiezfo3379gUg2o0qICh7sfu1b9++yM3NRUNDg1g3a3FxMQwNDUVuzfcy\nSpAyuvT4CbI8XTu6Gp3O1zdv4z1zk46uRqez0WZPR1ehU7px8UvYOC/u6Gp0KrE7rDq6CjKrQhPi\n1KxlXm9SVZ5c+7O1tQWfz0daWprYsvT0dACAo6OjsMzZ2Rm//PILLly4gDFjxgjLa2trkZ2dDXd3\n9xb3R12shBBC5Mbrwcn8kJempiZ8fHyQlJSEnJwcYXlVVRWio6NhZWUlHMEKANOmTQPHcfj8889F\nthMVFYWnT59i1qxZLe6PWpCEEEI61P79+3Hr1i0AwIMHD9DQ0ICPPvoIwPNrJGfPni2MjYiIQEJC\nAry9vbF06VJoaWkhKioKpaWliIuLE9nu0KFDhTdF8Pf3x5tvvokbN25g27ZtcHd3x8yZM1usFyVI\nohAOejodXQXShRgau3R0FYiCcMpt74jcvXs3zp8//3x7/z9RyZo1awAA7u7uIgnSwsICqampCA8P\nR2RkJOrqJ61gAAAgAElEQVTr6+Hg4IBTp07B09NTbNuff/45zMzM8M033yAuLg5GRkZYvHgxNmzY\n0Pqx0VRzsuE4js5BEqnROUgirdgdVq3ODdrZcByHMz2HyLyed9m1LnGs1IIkhBAiN065+05NSQmS\nEEKI3Noy6KazowRJCCFEbtSCJIQQQiSgFiQhhBAiAadECZIQQggRw6MESQghhIjjeJQgCSGEEDGc\nUvedsZQSJCGEELl15y7W7pv6CSGEkDagFiQhhBC50TlIQgghRILu3MVKCZIQQojc6DpIQgghRAKO\n132HsnTfIyOEENLuOB4n80OS+/fvY+HChejfvz/4fD5MTU2xZMkSPH78WCw2Ly8Pvr6+0NfXh6am\nJtzc3JCYmKjwY6MWJCGEELkp4hxkWVkZXFxcUFpaioULF2Lo0KG4cuUKdu7cieTkZKSmpkJNTQ0A\nUFBQAFdXV6ioqCAsLAza2tqIiorChAkTEB8fDy8vrzbXR4ASJCGEELkpYhTrpk2bcPv2bRw+fBjT\npk0Tlru6umLmzJn49NNPsXr1agDAypUrUVlZiaysLNjZ2QEA5syZgyFDhiAkJAS5ubltro8AdbES\nQgiRG8fjyfx4WWJiItTV1UWSIwBMmzYNfD4fMTExAIDq6mqcOHEC7u7uwuQIABoaGggODkZ+fj4y\nMjIUdmyUIAkhhMhNEecg6+rqoKqqKr5tjoOamhoKCwtRUVGBnJwc1NfXY9SoUWKxLi4uAIDMzEyF\nHRslSEIIIXLjKXEyP142dOhQVFRU4Pfffxcpz87OxqNHjwAAt27dQklJCQDA2NhYbBuCsuLiYsUd\nm8K2RAgh5LWjiBbkkiVLwOPxEBAQgPj4eNy+fRvx8fGYNm0alJWVwRjD06dPUVNTAwDg8/li2xC0\nQAUxikAJkhBCSIcaM2YMjhw5gidPnmDSpEkwMzPD5MmT4eXlhb/97W8AAG1tbairqwN43iX7stra\nWgAQxigCjWIlhBAiN2kmCrj4oAIXH/zVYsw777wDPz8/XL16FU+ePIG1tTUMDQ3h7OwMZWVlWFpa\n4smTJwAkd6MKyiR1v8qLEiQhhBC5SXOZh0svA7j0MhA+/yq3UGIcj8cTGZ167949XL58GR4eHlBV\nVYWtrS34fD7S0tLE1k1PTwcAODo6ynoIzaIuVkIIIXJT1Ew6L2tqasLixYvBGBNeA6mpqQkfHx8k\nJSUhJydHGFtVVYXo6GhYWVnByclJYcdGLUhCCCFyU8REAVVVVXB2doafnx/MzMzw+PFjHD58GJcu\nXcKmTZswbtw4YWxERAQSEhLg7e2NpUuXQktLC1FRUSgtLUVcXFyb6/IiSpCEEELkpojJyvl8PoYP\nH45Dhw6htLQU6urqcHZ2xunTp/HGG2+IxFpYWCA1NRXh4eGIjIxEfX09HBwccOrUKXh6era5Li+i\nBEkIIURuipiLVVlZGYcOHZI6ftCgQYiNjW3zfltDCZIQQojcFNHF2llRgiSEECK37nw/SEqQhBBC\n5EYtSEIIIUQCSpCEEEKIBN25i7X7HhkhhBDSBtSCJIQQIjfqYiWEEEIk6M5drJQgCSGEyI+jFiQh\nhBAihrpYCSGEEAmoi5UQQgiRgFqQhBBCiATUgiSEEEIk6M4tyO6b+gkhhLQ7jsfJ/JDk4cOHWLVq\nFWxsbKCpqQkjIyOMHj0ae/fuFYvNy8uDr68v9PX1oampCTc3NyQmJir82KgFSQghRH4K6GKtq6uD\nm5sb8vPzERgYiJEjR6K6uhqHDx9GUFAQbty4gcjISABAQUEBXF1doaKigrCwMGhrayMqKgoTJkxA\nfHw8vLy82lwfAY4xxhS2tdcAx3HI8nTt6GqQLmKjzZ6OrgLpImJ3WKGrfR1zHIey1YEyr9fz4z0i\nx3r27Fl4e3tj6dKl+M9//iMsb2howKBBg1BRUYG//voLABAQEIBjx44hKysLdnZ2AIDq6moMGTIE\nqqqqyM3NbdMxvYi6WAkhhHQodXV1AECfPn1EypWVlWFgYABNTU0AzxPhiRMn4O7uLkyOAKChoYHg\n4GDk5+cjIyNDYfWiLlZCCCFyU8QoVldXV7z55pvYvHkzzMzM4OzsjJqaGuzduxeXLl3C119/DQDI\nyclBfX09Ro0aJbYNFxcXAEBmZiacnJzaXCeAEiQhhJA2UNQo1hMnTiAkJAQBAQHCMi0tLRw9ehST\nJ08GAJSUlAAAjI2NxdYXlBUXFyukPgB1sRJCCGkLHk/2x0saGhrwzjvvYM+ePVi+fDmOHTuG6Oho\nWFpaYsaMGTh79iwAoKamBgDA5/PFtqGqqioSowjUgiSEECI3RbQgv/nmGxw/fhy7du3CggULhOUz\nZszA0KFDMX/+fBQUFAjPVdbV1Ylto7a2FsD/zmcqAiVIQgghcuO41jsif71ZjF8LS5pdfvbsWXAc\nh6lTp4qUq6mp4a233sKOHTtw69Yt9O3bF4DkblRBmaTuV3lRgiSEECI/KVqQYyz7YYxlP+HzzYlZ\nIssbGhrAGENjY6PYuoKyxsZG2Nrags/nIy0tTSwuPT0dAODo6ChT9VtC5yAJIYTIjePxZH68zNnZ\nGQCwZ88ekfJHjx7h+PHj0NfXh6WlJTQ1NeHj44OkpCTk5OQI46qqqhAdHQ0rKyuFjWAFqAVJCCGk\nDRRxDjIkJATffvstwsPDceXKFbi6uqKiogJRUVG4f/8+duzYAe7/b8wcERGBhIQE4cQCWlpaiIqK\nQmlpKeLi4tpclxdRgiSEECI/Kc5BtsbAwADp6elYv3494uPjceTIEaipqcHe3h6fffYZfH19hbEW\nFhZITU1FeHg4IiMjUV9fDwcHB5w6dQqenp5trsuLKEE2Y926ddiwYQOKiopgYmLS0dUhhJBOSVHX\nQfbp0we7du2SKnbQoEGIjY1VyH5bQgmSEEKI/Lrx/SC775ERQgghbUAtSEIIIXITDJ7pjjq0BVlU\nVAR/f39oa2tDR0cHvr6+KCoqgpmZGTw8PMTio6OjMWLECKirq0NXVxcTJkxAamqqxG1LG9vU1ISI\niAgMGDAAampqsLW1xaFDhxR+rIQQ0i0pYKq5zqrDalpeXo6xY8ciLi4O8+bNw+bNm6GhoQEPDw/U\n1NSI/SoJCwvDggULwOfzERERgWXLluH69evw8PBAfHy83LGhoaFYvXo1zMzMsGXLFvj6+iIkJAQ/\n/fRTu78GhBDS1XE8TuZHV9FhN0xesWIFtm7dioMHD2LGjBnC8rCwMGzZsgXu7u44d+4cACAvLw82\nNjYYM2YMzp07hx49nvcMl5aWYvDgwdDV1UVBQQF4PJ5csV5eXjhz5owwKV++fBkODg7gOA6FhYUi\no1jphslEFnTDZCKtrnrD5Cc7wmReTyvkky5xrB3Wgvzpp5/Qt29fkeQIAMuXLxeLPX78OIDnSVWQ\n8IDnw4KDgoJw69YtZGdnyx0bGhoq0mK1t7eHt7d3l/gDEkJIh+Jxsj+6iA5LkIWFhbC0tBQrNzIy\ngo6OjlgsAAwZMkQsfvDgwQCAmzdvyhwr+HfQoEFisTY2NtIdCCGEvMY4jifzo6ugUaxy+PrmbeH/\nHfR04Kin00I0IYSIe1B8AQ+LL3R0NdquC7UIZdVhCdLMzAx//PEHGGMi3ZtlZWV4/PixSKyFhQUA\n4OrVqxgwYIDIsuvXrwMAzM3NRf6VJfbGjRvNxkrynjnNrEMIaRsjYxcYGbsIn+dlbO/A2shP0uTj\n3UWHHdnkyZNRWlqKw4cPi5Rv3bpVYizHcdiyZYvI7VBKS0sRExMDMzMz2NvbAwDefvttmWM//fRT\nNDU1CWMvXbokvD8ZIYSQFnCc7I8uosNakGFhYTh06BCCgoJw8eJFWFtbIyUlBWlpaTA0NBRJTlZW\nVvjggw+wefNmuLm5ISAgAE+ePME333yDmpoaHD58WBgvS6y1tTVCQkKwfft2eHp6ws/PD2VlZdix\nYweGDx+Oy5cvd8hrQwghXUY3bkF2WII0MDDAr7/+imXLlmH37t3gOE54aYezszPU1NRE4iMjI2Fp\naYmvvvoKK1euhIqKCkaOHIkjR45g9OjRcsd+8cUX6N27N7755husWLECVlZW+Oqrr5Cfny8c7UoI\nIaQZXahFKKsOuw6yOeXl5TAyMsLChQvx1VdfdXR1xNB1kEQWdB0kkVZXvQ6yZt9GmddTn/NhlzjW\nDm0bP336VKwsMjISAPDGG2+86uoQQgjpAOvWrQOPx2v2oaKiIhKfl5cHX19f6OvrQ1NTE25ubkhM\nTFR4vTr0Mo+33npLOGimqakJCQkJiIuLw+jRo0VukEkIIaSTUsB1jf7+/rCyshIr//3337FlyxZM\nnjxZWFZQUABXV1eoqKggLCwM2traiIqKwoQJExAfHw8vL68210egQxOkj48P9u3bh2PHjuHp06fo\n378/li9fjrVr19IIUkII6QoUcB2kra0tbG1txcrPnz8PAHj33XeFZStXrkRlZSWysrJgZ2cHAJgz\nZw6GDBmCkJAQ5Obmtrk+Ah3axRoaGors7Gw8evQIdXV1+PPPP4WTlhNCCOn82msmnerqahw5cgT9\n+/fHxIkThWUnTpyAu7u7MDkCgIaGBoKDg5Gfn4+MjAyFHVv3HZ9LCCGk/bXTXKw//PADnjx5gsDA\nQGGPYk5ODurr6zFq1CixeBeX55MuZGZmKuzQaKo5Qggh8munuVW//fZb8Hg8zJs3T1hWUlICADA2\nNhaLF5QVFxcrrA6UIAkhhMivHcaL5OXlITU1FePHj4epqamwvKamBgDA5/PF1lFVVRWJUQRKkIQQ\nQuTXDjPpfPvttwCA4OBgkXJ1dXUAQF1dndg6tbW1IjGKQAmSEEKI/KToYk2++geSr/0p1eYaGxux\nb98+GBoaYsqUKSLL+vbtC0ByN6qgTFL3q7woQRJCCJGfFINu3Oys4Gb3v+scP/7+dLOxP/30E8rK\nyrBkyRIoKyuLLLO1tQWfz0daWprYeunp6QAAR0dHaWveKhrFSgghRH4cT/ZHCwTdqy9e+yigqakJ\nHx8fJCUlIScnR1heVVWF6OhoWFlZwcnJSWGHRi1IQggh8lPgIJ2SkhKcOnUKLi4uGDJkiMSYiIgI\nJCQkwNvbG0uXLoWWlhaioqJQWlqKuLg4hdUFoARJCCGkk9izZw8YY2KDc15kYWGB1NRUhIeHIzIy\nEvX19XBwcMCpU6fg6emp0Pp0urt5dHZ0Nw8iC7qbB5FWV72bx9OfZL/rkprPP7rEsVILkhBCiPy6\n8bzZlCAJIYTIr51m0ukMKEESQgiRXztMFNBZUIIkhBAiP+piJYQQQiSgLlZCCCFEAmpBEkIIIRLQ\nOUhCCCFEHKMWJCGEECIBnYMkhBBCJOjGCbL7HhkhhBDSBtSCJIQQIjc6B0kIIYRI0o27WClBEkII\nkV83bkF239RPCCGk/fF4sj+aUVFRgeXLl8PS0hJqamro2bMnPD098euvv4rE5eXlwdfXF/r6+tDU\n1ISbmxsSExMVfmjUgiSEECI3RZ2DvHXrFtzd3VFTU4N3330XVlZWePToEa5cuYKSkhJhXEFBAVxd\nXaGiooKwsDBoa2sjKioKEyZMQHx8PLy8vBRSH4ASJCGEkLZQ0DnI2bNno6mpCTk5OejVq1ezcStX\nrkRlZSWysrJgZ2cHAJgzZw6GDBmCkJAQ5ObmKqQ+AHWxEkIIaQPG8WR+vCw5ORmpqalYsWIFevXq\nhYaGBtTU1IjFVVdX48SJE3B3dxcmRwDQ0NBAcHAw8vPzkZGRobBjowRJCCFEfhwn++MlP//8MwCg\nf//+8PHxgbq6OjQ1NWFtbY2DBw8K43JyclBfX49Ro0aJbcPFxQUAkJmZqbBDowRJCCFEbopoQebl\n5QEA5s+fj0ePHmHfvn3YvXs3VFRU8Pe//x179uwBAOG5SGNjY7FtCMqKi4sVdmx0DpIQQoj8FDBI\n58mTJwAAbW1tJCYmokeP56nJ19cX5ubmWLVqFebOnSvsduXz+WLbUFVVBQCJXbPyogRJCCFEflIM\n0knJykFKVk6zy9XU1AAAM2bMECZHANDV1YWPjw/279+PvLw8qKurAwDq6urEtlFbWwsAwhhFoARJ\nCCGkXY11sMNYh/8NqomMOiiyvF+/fgCA3r17i63bp08fAMCjR49a7EYVlEnqfpUXnYMkhBAiN8Zx\nMj9eJhhgc+fOHbFld+/eBQD07NkTQ4cOBZ/PR1pamlhceno6AMDR0VFhx0YJkhBCiPw4nuyPl/j6\n+kJLSwsHDhxAdXW1sLy0tBSxsbGwtraGubk5NDU14ePjg6SkJOTk/K/LtqqqCtHR0bCysoKTk5PC\nDo26WAkhhMiNoe2DdHR1dbF161a89957GDlyJObNm4e6ujrs3LkTjY2N2LZtmzA2IiICCQkJ8Pb2\nxtKlS6GlpYWoqCiUlpYiLi6uzXV5ESVIQgghcpN02YY85s+fD0NDQ2zevBkffvgheDweXF1dceTI\nEZHrHi0sLJCamorw8HBERkaivr4eDg4OOHXqFDw9PRVSFwFKkIQQQuSnwNtdTZkyBVOmTGk1btCg\nQYiNjVXYfptDCZIQQojc6IbJhBBCiASK6mLtjChBEkIIkR+1IP+nsLAQZ8+eRVlZGWbOnIkBAwag\nvr4e9+7dQ69evSROAUQIIaR76s4tSJmObMWKFRg4cCDee+89rFmzBoWFhQCAp0+fwsbGBl999VW7\nVJIQQkjnxMDJ/OgqpE6QX3/9NbZu3YpFixbhzJkzYIwJl+no6ODtt9/GyZMn26WShBBCOidF3M2j\ns5K6i/Wrr76Cr68vPv/8czx8+FBsua2tLc6fP6/QyhFCCCEdRepUnp+fD29v72aXGxkZSUychBBC\nujEF3DC5s5K6BamqqioyR97Lbt++DV1dXYVUihBCSNfAuvGU3lIfmZOTE44dOyZxWW1tLfbv34/R\no0crrGKEEEI6P0XczaOzkjpBrlixAmlpaZg9e7ZwFvXS0lKcOnUK48aNw507d7B8+fJ2qyghhJDO\nhwbpABg/fjx27dqFxYsX49ChQwCAv//97wAAPp+P6OhouLq6tk8tCSGEdEpd6bINWck0UcCCBQvg\n4+ODH3/8ETdu3ABjDFZWVggICFDoXZwJIYR0DV2pRSgrmWfS6dOnD/75z3+2R10IIYR0MV3pnKKs\num/qJ4QQ0u4UNZMOj8eT+NDS0hKLzcvLg6+vL/T19aGpqQk3NzckJiYq/NikbkF6eHiAa+GXAmMM\nHMfh3LlzCqkYIYSQzk+RXaxubm5YsGCBSJmysrLI84KCAri6ukJFRQVhYWHQ1tZGVFQUJkyYgPj4\neHh5eSmsPlInyMLCQnAcJzLFXGNjI0pLS8EYg6GhITQ0NBRWMUIIIZ2fIgfpmJubY+bMmS3GrFy5\nEpWVlcjKyoKdnR0AYM6cORgyZAhCQkKQm5ursPpInfqLiopQWFiIoqIi4ePu3buorq7Gxx9/DB0d\nHaSmpiqsYoQQQjo/RV7mwRhDQ0MDqqqqJC6vrq7GiRMn4O7uLkyOAKChoYHg4GDk5+cjIyNDYcfW\n5raxqqoqVq5cCRcXF4SGhiqiToQQQl5DP/74I9TV1aGtrY1evXph8eLFqKysFC7PyclBfX09Ro0a\nJbaui4sLACAzM1Nh9VHYDZPHjBmDlStXKmpzhBBCugBFdbE6OzsjICAAlpaWqKysRFxcHLZv347z\n588jLS0NGhoaKCkpAQCJlxUKyoqLixVSH0CBCbKoqAj19fWK2hwhhJAuQFGDdNLT00Wez549G3Z2\ndli9ejW++OILrFq1CjU1NQCeT07zMlVVVQAQxiiC1Any9u3bEssrKirwyy+/4IsvvoC7u7ui6tWp\nLdP/T0dXgXQRq49N6egqkC4itqMrICdpWpDp6em4cOGCzNv+4IMPsH79evz8889YtWoV1NXVAQB1\ndXVisbW1tQAgjFEEqROkmZlZi8utra3x5ZdftrU+hBBCuhBpJgpwGTUKLi+cN/xy2zaptt2jRw/0\n6dNHeCvFvn37ApDcjSooU+SsblInyDVr1oiVcRwHfX19WFtbY/z48eDxaN4BQgh5nTDWfjPp1NbW\n4u7du8J5vm1tbcHn85GWliYWK+iidXR0VNj+pU6Q69atU9hOCSGEdA+KuB9kRUUF9PX1xco//PBD\nPHv2DD4+PgAATU1N+Pj44OjRo8jJyRFe6lFVVYXo6GhYWVnBycmpzfURkCpBPnnyBMOGDcPixYux\nZMkShe2cEEJI16aIUawbN27EhQsX4OHhgf79+6Oqqgo///wzkpKSMHLkSJH5vyMiIpCQkABvb28s\nXboUWlpaiIqKQmlpKeLi4tpclxdJlSC1tLRQUVEBTU1Nhe6cEEJI16aIBOnh4YEbN25g7969KC8v\nh5KSEqysrLBp0yaEhoZCRUVFGGthYYHU1FSEh4cjMjIS9fX1cHBwwKlTp+Dp6dnmurxI6i5WFxcX\nZGZmIjg4WKEVIIQQ0nUpIkFOnjwZkydPljp+0KBBiI1t/3G/UnceR0ZG4vvvv8fu3btF5mMlhBDy\n+lLU3Tw6oxZbkLdv34ahoSHU1dURGhoKPT09BAcHIywsDBYWFhKvN6G7eRBCyOujPUexdrQWE6SZ\nmRkOHDiAmTNnCu/mYWJiAgC4d++eWHxLt8MihBBCuhKpz0EWFRW1YzUIIYR0RV2py1RWCpuLlRBC\nyOuHEiQhhBAiwWudIFNSUtDY2Cj1BufMmdOmChFCCOk6uvMgHY61cM2GrHOrchyHZ8+etblSnRnH\ncXB/57eOrgbpIlan0XXDRDpvlFzrcpfQcRyHy/llMq9nb9WzSxxrqy3IBQsWYOTIkVJtjEaxEkLI\n6+W17mJ1c3PDzJkzX0VdCCGEdDHduYuVBukQQgiR22vdgiSEEEKaQy1IQgghRILXtgXZ1NT0qupB\nCCGkC+rOLci23wqaEEIIUbCamhqYm5uDx+OJ3DBZIC8vD76+vtDX14empibc3NyQmJio0DpQgiSE\nECK3Jjke0lizZg0ePnwIQPwSwoKCAri6uuLChQsICwvDli1bUFVVhQkTJiAhIUEBR/UcnYMkhBAi\nt/boYr106RK++OILbNmyBaGhoWLLV65cicrKSmRlZcHOzg7A81nchgwZgpCQEOTm5iqkHtSCJIQQ\nIjdF3zD52bNnmD9/Pt58801MmTJFbHl1dTVOnDgBd3d3YXIEAA0NDQQHByM/Px8ZGRkKOTZKkIQQ\nQuTGGCfzoyWfffYZ8vLysH37donT0eXk5KC+vh6jRo0SW+bi4gIAyMzMVMixUYIkhBAiN0W2IAsL\nC7F27VqsXbsWJiYmEmNKSkoAAMbGxmLLBGXFxcUKODI6B0kIIaQNmhQ45/jChQthaWkp8byjQE1N\nDQCAz+eLLVNVVRWJaStKkIQQQuQmzUQBly8mIzsjpcWYAwcO4OzZs0hJSYGSklKzcerq6gCAuro6\nsWW1tbUiMW1FCZIQQojcpBnFOtxpHIY7jRM+37tzk8jyuro6hIaGYtKkSejVqxf+/PNPAP/rKn30\n6BEKCgpgaGiIvn37iix7kaBMUverPOgcJCGEELkxJvvjZU+fPsXDhw9x8uRJDBw4EFZWVrCysoKH\nhweA563LgQMH4ttvv4WdnR34fD7S0tLEtpOeng4AcHR0VMixUQuSEEKI3JoUMBerpqYmfvjhB7EJ\nAcrKyvCPf/wDb775Jt59913Y2dlBQ0MDPj4+OHr0KHJycoSXelRVVSE6OhpWVlZwcnJqc50ASpCE\nEELaQBETBfTo0QP+/v5i5UVFRQAACwsL+Pn5CcsjIiKQkJAAb29vLF26FFpaWoiKikJpaSni4uLa\nXB9hvRS2JUIIIa8dSV2m7c3CwgKpqakIDw9HZGQk6uvr4eDggFOnTsHT01Nh+6EESQghpFMyMzNr\n9q5SgwYNQmxsbLvunxIkIYQQub2294MkhBBCWqLIiQI6G0qQhBBC5Nadb5hMCZIQQojcOmKQzqtC\nCZIQQojcFHEdZGdFCZIQQojcqAVJCCGESEDnIAkhhBAJaBQrIYQQIgF1sRJCCCES0EQBhBBCiATd\nuYuV7gdJCCGESEAtSEIIIXLrzucgqQVJCCFEbozJ/nhZXl4eZs2aBRsbG+jq6kJDQwNWVlYICQlB\nYWGhxHhfX1/o6+tDU1MTbm5uSExMVPixUQuSEEKI3JoUcB1kcXEx7t27B39/f/Tr1w89evRATk4O\nYmJicOjQIVy6dAkDBgwAABQUFMDV1RUqKioICwuDtrY2oqKiMGHCBMTHx8PLy6vN9RGgBEkIIURu\niuhi9fT0lHijYzc3NwQEBGDv3r1Yt24dAGDlypWorKxEVlYW7OzsAABz5szBkCFDEBISgtzc3LZX\n6P9RFyshhBC5KaKLtTkmJiYAABUVFQBAdXU1Tpw4AXd3d2FyBAANDQ0EBwcjPz8fGRkZCjs2SpCE\nEELk1sRkfzSnrq4ODx8+xN27d3HmzBm89957MDExwbvvvgsAyMnJQX19PUaNGiW2rouLCwAgMzNT\nYcdGCZIQQojcGONkfjQnKioKPXv2hImJCSZOnAhlZWWkpKSgV69eAICSkhIAgLGxsdi6grLi4mKF\nHRudgySEECI3RV7mMWXKFAwePBhVVVW4dOkStm3bhnHjxuHs2bMwNzdHTU0NAIDP54utq6qqCgDC\nGEWgBEkIIURuipxJx9jYWNgSnDx5Mvz9/eHk5ISlS5fi+PHjUFdXB/C8K/ZltbW1ACCMUQRKkIQQ\nQuQmTQsyNzsJudlJMm/b1tYWw4cPR3JyMgCgb9++ACR3owrKJHW/yosSJCGEELlJkyCth7nDepi7\n8PmJfeul3v7Tp0/B4z0fLmNraws+n4+0tDSxuPT0dACAo6Oj1NtuDQ3SIYQQ0qHu378vsTwxMRFX\nr14VXvyvqakJHx8fJCUlIScnRxhXVVWF6OhoWFlZwcnJSWH1ohYkIYQQuSniHOTChQtx7949eHp6\nwm25qAMAABRNSURBVMTEBLW1tcjKysJ3332HXr164ZNPPhHGRkREICEhAd7e3li6dCm0tLQQFRWF\n0tJSxMXFtb0yL6AESQghRG6KGMU6c+ZM7Nu3D/v378eDBw/AcRzMzc2xePFirFixAkZGRsJYCwsL\npKamIjw8HJGRkaivr4eDgwNOnTolcTaetugyCXLPnj2YN28ekpKS4Obm1u77S0pKgqenJ2JiYjB3\n7tx23x8hhHRFTU1t38bUqVMxdepUqeMHDRqE2NjYtu+4FV0mQXYUjuu+d8smhJC26s63u6IESQgh\nRG6UIAkhhBAJFDlRQGfT5S7zaGhowLp162BqagpVVVUMGzYM3333nUjMmTNnMG3aNJibm0NdXR16\nenqYMGGC8GLTlx0/fhz29vZQU1ODiYkJ1qxZg4aGhldxOIQQ0qUxxmR+dBVdrgUZFhaGmpoaLFq0\nCIwxxMTEYMaMGaitrRUOptm7dy8ePXqEwMBA9OvXD3fv3kV0dDS8vLyQmJiIMWPGCLd37Ngx+Pv7\nw9zcHGvXroWSkhJiYmJw8uTJjjpEQgjpMrpQvpNZl0uQ5eXlyMnJgZaWFoDn18/Y2dkhNDQU06ZN\ng6qqKqKiosTm41u4cCGGDBmCiIgI4bUyz549w7/+9S8YGhri4sWL0NfXBwC89957IvcaI4QQIpki\nRrF2Vl2ui/X9998XJkcA0NbWxsKFC/HXX38hKSkJgOhktVVVVSgvLwePx4OzszMuXLggXJaVlYW7\nd+8iKChImBxf3CYhhJCWtecNkztal2tB2tjYNFtWWFgIACgoKMDq1atx+vRpPH78WCRWMKcfANy8\neRPA82tqpNkPIYQQUd15kE6XS5CtqaqqgpubG54+fYqlS5fC1tYWWlpa4PF42LRpExITE9u8j8Jr\n0cL/6xqNgF7PEW3eJiHk9fJ7XTV+r6vu6GqQFnS5BHn9+nX4+PiIlQGAubk5EhISUFpaKnEGnFWr\nVok8t7CwAADcuHFD4n6aM2BIsFx1J4QQgWF8DQzjawif76960IG1kV9X6jKVVZc7B7lz505UVlYK\nnz9+/Bi7du2Cnp4exo0bByUlJQBA00tnjs+cOYOLFy+KlDk4OKBfv36IiYlBeXm5sLyyshK7du1q\nx6MghJDugTUxmR9dRZdrQRoZGcHFxQVBQUHCyzwEl3Goqqpi7Nix6N27N5YtW4aioiIYGxsjOzsb\nBw4cgK2tLa5cuSLcFo/Hw2effYaAgAA4Oztj/vz5UFJSwu7du2FoaIg7d+504JESQkjn14Xyncy6\nVILkOA6ffPIJkpOTsWPHDty/fx/W1tY4ePAgpk+fDgDQ0dHB6dOnsWLFCmzbtg2NjY1wdHREfHw8\noqOjcfXqVZFt+vv748cff8SGDRuwbt069OrVC4GBgRg7diy8vb074jAJIaTL6M5drBzrStMadAIc\nx8H9nd86uhqki1idRueriXTeKLnWpWaZAZ5/H276rlHm9VZN69EljrXLnYMkhBDSeSjiOsj8/Hys\nWbMGI0eORM+ePaGtrQ17e3ts2rQJNTU1YvF5eXnw9f2/9u49JoqrYQP4M+uri7ss3lAQ3lhWBUFQ\nWy4ieFvASm+oDUGjrVSsrTW0VrwUL21FrMVGW42X2goqBo2mfVU0n9ZqrNivKARtFbWggGIVjWjr\nV0VcF93z/eHL1nUHgWHlos8vmcQ9c2bmDIl5cs6cOTMKHTt2hKOjI4YMGWKXNxQexYAkIiLF7BGQ\n69evx/Lly+Hp6Yn58+dj6dKl6NWrFz7++GOEhobCaDRa6paUlCA0NBS5ublITEzEkiVLUFFRgcjI\nSBw4cMCu99ainkESEVHzYrbDUGlMTAzmzZtntUrau+++C09PTyxatAjr1q1DfHw8AGDOnDm4efMm\njh07ZlkSNDY2Fr6+voiPj0dhYWGD21ONPUgiIlJMmOu/PSogIMAqHKuNHj0aAHD69GkAwO3bt7Fr\n1y4YDAar9bK1Wi0mTZqEs2fPIi8vz273xoAkIiLFnuTnri5dugQAcHFxAQDk5+fDZDIhJCTEpm5w\ncDAA4OjRo3a4qwc4xEpERIo9qa953L9/HwsXLkTr1q0xbtw4AMDly5cBAO7u7jb1q8vKysrs1gYG\nJBERNTvTpk1DTk4OUlJS4OnpCQCWGa1qtdqmvoODg1Ude2BAEhGRYk/ifcZPPvkEq1evxuTJk5GY\nmGgpr/6U4d27d22OqZ7p+ui3gBuCAUlERIrVZam5C4WHcKHw5zqdLykpCYsWLcLEiROxZs0aq31u\nbm4A5IdRq8vkhl+VYkASEZFidVl8vJvXEHTzGmL5/b87P5Otl5SUhOTkZEyYMAFpaWk2+/v06QO1\nWo3Dhw/b7MvJyQEABAYG1rXpteIsViIiUsweCwUAQHJyMpKTkxEbG4v169fL1nF0dERUVBSysrKQ\nn59vKa+oqEBaWhq8vLwQFBRkt3tjD5KIiBQz2+FzHqtXr0ZSUhK6deuGiIgIbNq0yWq/q6srhg0b\nBgBISUnBgQMHMHz4cCQkJECn0yE1NRVXrlzB7t27G9yWhzEgiYhIMXtM0jl69CgkScLFixdtPnQP\nAAaDwRKQPXr0QHZ2NmbPno3FixfDZDIhICAAe/fuRXh4eIPb8jAGJBERKSa3Mk59bdiwARs2bKhz\nfW9vb2RmZjb8wrVgQBIRkWL2WIu1uWJAEhGRYi3hu45KMSCJiEgxe0zSaa4YkEREpNhT3IHke5BE\nRERy2IMkIiLF6rKSTkvFgCQiIsU4i5WIiEgGe5BEREQyGJBEREQynuJ8ZEASEZFy7EESERHJ4Eo6\nREREMriSDhERkYynuQfJlXSIiEgxYRb13h6VkpKCmJgYdO/eHSqVCnq9/rHXPHPmDEaNGoWOHTvC\n0dERQ4YMwcGDB+1+b+xBEhGRYvaYpDNv3jx06tQJ/v7++PvvvyFJUo11S0pKEBoaijZt2iAxMRFO\nTk5ITU1FZGQkfvjhB0RERDS4PdUYkERE1KTOnTsHDw8PAICfnx8qKytrrDtnzhzcvHkTx44dQ9++\nfQEAsbGx8PX1RXx8PAoLC+3WLg6xEhGRYmYh6r09qjoca3P79m3s2rULBoPBEo4AoNVqMWnSJJw9\nexZ5eXn2ujUGJBERKWePZ5B1lZ+fD5PJhJCQEJt9wcHBAICjR48qPv+jOMRKRESKNeYs1suXLwMA\n3N3dbfZVl5WVldntegxIIiJSrDHfg6x+NqlWq232OTg4WNWxBwYkEREp1phLzWk0GgDA3bt3bfYZ\njUarOvbAgCQiIsXqMsR69Y8jKP/jSIOv5ebmBkB+GLW6TG74VSkGJBERKSbM5lrrdPl3MLr8O9jy\n+1T2MkXX6tOnD9RqNQ4fPmyzLycnBwAQGBio6NxyOIuViIgUM5tFvTelHB0dERUVhaysLOTn51vK\nKyoqkJaWBi8vLwQFBdnjtgCwB0lERA1gj1msGRkZuHDhAgDg2rVrqKqqwmeffQbgwTuSb775pqVu\nSkoKDhw4gOHDhyMhIQE6nQ6pqam4cuUKdu/e3eC2PIwBSUREitljks769etx6NAhALAsM/fpp58C\nAAwGg1VA9ujRA9nZ2Zg9ezYWL14Mk8mEgIAA7N27F+Hh4Q1uy8MYkEREpJg9ArK+C417e3sjMzOz\nwdetDZ9BEhERyWAPkoiIFDOL2mextlQMSCIiUqwxFwpobAxIIiJSjAFJREQkozEXK29sDEgiIlLM\nXIeVdFoqBiQRESnGIVYiIiIZgrNYiYiIbLEHSUREJIMBSUREJIMLBRAREclgD5KIiEhGXT6Y3FJx\nsXIiIiIZDEgiIlJMmEW9NzlmsxnLli2Dt7c32rZti27dumHmzJmorKxs5Dv6BwOSiIgUE8Jc701O\nQkICZsyYAT8/P6xatQoxMTFYsWIFoqKimmw5Oz6DJCIixcx2mKRz+vRprFy5EtHR0fj+++8t5Xq9\nHlOnTsXWrVsxduzYBl+nvtiDJCIixYTZXO/tUVu2bAEATJs2zar8nXfegUajwaZNmxrlXh7FgCS7\nuFH+a1M3gVqQE3dvN3UTyE7s8QwyLy8PrVq1Qv/+/a3K1Wo1+vXrh7y8vMa6HSsMSLKL/7vGgKS6\nY0A+PezxDPLy5ctwdnZG69atbfa5u7vj+vXruHfvXmPcjhU+gyQiIsXssVBAZWUl1Gq17D4HBwdL\nHScnpwZfqz4YkEREpJg9FgrQaDS4fv267D6j0QhJkqDRaBp8nfpiQNbT0KFDkfWfkKZuRrN0oWBd\nUzeh2clq6gY0YxkV15q6Cc3K0KFDm7oJimT/j6Hexzg6Olr9dnNzQ2FhIaqqqmyGWcvKyuDs7Ix/\n/avx44oBWU9ZWVlN3QQiombBXu8n9u/fH/v370dubi4GDRpkKTcajTh+/DgMBoNdrlNfnKRDRERN\nasyYMZAkCcuXL7cqT01NxZ07d/DGG280Sbsk0VRLFBAREf3X1KlTsWrVKrz++ut4+eWXUVBQgJUr\nV2LQoEH46aefmqRNDEgiImpyZrMZy5cvx9q1a1FaWorOnTtjzJgxSE5ObpIJOgCHWIkarLS0FCqV\nCgsWLHhsWXMyYcIEqFT870/Nh0qlwvTp01FYWAij0YiLFy9i6dKlTRaOAAOSWrCsrCyoVCqrTafT\nITAwECtWrIC5kb9TJ0lSncpqU1paiqSkJJw4ccIezaqRkrYRPUs4i5VavHHjxuGVV16BEAJlZWVI\nT0/HtGnTcPr0aXz77bdN0iYPDw8YjUa0atWq3seWlpYiOTkZ3bt3R79+/Z5A6x7g0xWix2NAUovn\n7++PcePGWX5PmTIFPj4+SEtLw8KFC9GlSxebY27dugWdTvdE29WmTZsGHc8AI2paHGKlp45Op8OA\nAQMAAOfOnYOHhwfCwsLw22+/ITIyEu3bt7fqmRUVFWH8+PHo2rUr1Go19Ho9PvroI9kPtf7yyy8Y\nOHAgNBoNXF1d8cEHH6CiosKm3uOeQW7btg0GgwEdOnSAVquFt7c3PvzwQ1RVVSE9PR3h4eEAgLi4\nOMvQcVhYmOV4IQTWrFmDgIAAaLVa6HQ6hIeHy76jazQaMWvWLLi5uUGj0SA4OBj79u2r99+U6FnE\nHiQ9dYQQKC4uBgA4OztDkiT88ccfiIiIwOjRoxETE2MJtWPHjiE8PBwdO3bElClT4O7ujuPHj2PF\nihXIzs7GoUOHLCt45ObmYtiwYWjXrh1mz56Ndu3aYevWrcjOzq6xLY8+55s3bx5SUlLg6+uL6dOn\no2vXriguLsb27duxcOFCDB06FHPnzsXnn3+OyZMnY/DgwQAAFxcXyznGjx+PrVu3IiYmBm+//TaM\nRiM2b96MF198Edu3b0dUVJSl7tixY7Fz506MGDECkZGRKC4uRnR0NPR6PZ9BEtVGELVQBw8eFJIk\nieTkZHHt2jVRXl4uTpw4ISZNmiQkSRKhoaFCCCGee+45IUmSWLdunc05+vbtK3x8fERFRYVV+Y4d\nO4QkSSI9Pd1SFhISItRqtSgqKrKUmUwm0b9/fyFJkliwYIGl/Pz58zZlubm5QpIkERERIe7evVvr\nfW3cuNFm3/bt24UkSSItLc2q/N69eyIwMFDo9XpL2Y8//igkSRJxcXFWdTMzM4UkSUKlUtXYBiIS\ngkOs1OLNnz8fXbp0gYuLC55//nmkp6dj5MiRyMzMtNTp1KkT4uLirI47efIkTp48ibFjx+LOnTu4\nfv26ZaseRq0ejiwvL0dOTg5GjhyJnj17Ws7RunVrJCQk1KmdmzdvBgCkpKQofj65adMm6HQ6jBgx\nwqq9N27cwGuvvYbS0lJL77n6/mfNmmV1jpEjR8LLy0vR9YmeJRxipRZv8uTJiImJgSRJ0Gq18PLy\nQvv27a3q9OjRw2ZIsaCgAMCDgJ0/f77sucvLywE8eJYJAN7e3jZ1fHx86tTOoqIiqFSqBs1MLSgo\nwK1bt6yGXB8mSRKuXr2Knj174ty5c2jVqpVsGPr4+KCoqEhxO4ieBQxIavE8PT0tE1tqIveysfjv\nLNGZM2fipZdekj2uQ4cODW/gQyRJatCzPyEEOnfujC1bttRYx9fXV/H5iegfDEh6ZlX3rFQqVa0B\nq9frAfzT63zY77//Xqfr9erVC3v37sXx48cRFBRUY73HBainpyf27NmD4OBgaLXax16ve/fu2Ldv\nH86cOYPevXtb7ZO7DyKyxmeQ9Mx64YUX4Ofnh2+++Qbnz5+32X/v3j3cuHEDwINZpAMGDMDOnTut\nhiZNJhOWLVtWp+tVv6s5d+5cVFVV1Viv+lt5f/75p82+t956C2azGXPmzJE99urVq5Z/jxo1CgCw\nZMkSqzqZmZk4e/ZsndpM9CxjD5KeaRkZGQgPD0ffvn0xceJE9O7dG5WVlSguLsaOHTuwePFixMbG\nAgC++uorGAwGDBw4EPHx8ZbXPO7fv1+nawUFBSExMRFffPEF/P39MWbMGLi4uOD8+fPYtm0b8vLy\n4OTkBF9fX+h0Onz99dfQaDRo164dXFxcEBYWhujoaMTFxWHVqlX49ddf8eqrr8LZ2RmXLl3CkSNH\nUFJSgpKSEgDA8OHDERUVhY0bN+Kvv/5CZGQkSkpKsHbtWvj5+eHUqVNP7O9K9FRo6mm0REpVvw7x\n5ZdfPraeh4eHCAsLq3H/hQsXxHvvvSc8PDxEmzZtRKdOnURgYKCYO3euuHTpklXdn3/+WYSGhgoH\nBwfh6uoq3n//fXHq1Kk6veZRbcuWLWLgwIFCp9MJrVYrfHx8REJCgjCZTJY6e/bsEf7+/sLBwUFI\nkmTT/oyMDDF48GDh5OQkHBwchF6vF9HR0eK7776zqnfnzh0xY8YM4erqKtq2bSuCg4PF/v37xYQJ\nE/iaB1Et+LkrIiIiGXwGSUREJIMBSUREJIMBSUREJIMBSUREJIMBSUREJIMBSUREJIMBSUREJIMB\nSUREJIMBSUREJIMBSUREJOP/AfFxhA3/oZisAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117b58210>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_probs = clf_most.predict_proba(X_test)[:,0]\n",
      "thres = .92 # This can be set to whatever you'd like\n",
      "y_pred[y_probs<thres] = 'good'\n",
      "y_pred[y_probs>=thres] = 'bad'\n",
      "cm = confusion_matrix(y_test, y_pred, labels)\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "good/good: 98.55% (68/69)\n",
        "good/bad: 1.45% (1/69)\n",
        "bad/good: 20.93% (9/43)\n",
        "bad/bad: 79.07% (34/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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okZGRuHDhArKzs1FcXAwzMzPRKGhp8vPzERYWhpSUFDQ0NGDkyJFYv3691FnA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\nNWvWyMY4OzsjOjoaWVlZKCgosJRXVlYiPT0d3t7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       "text": [
        "<matplotlib.figure.Figure at 0x117b43b50>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Using a different classifier\n",
      "<font size=3>\n",
      "Let's try a different classifier: Extra Trees Classifier (like RandomForest, but even more random).<br>\n",
      "http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf_most = sklearn.ensemble.ExtraTreesClassifier(n_estimators=50)\n",
      "X = df_all.as_matrix([item[0] for item in importances[:20]])\n",
      "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=my_tsize, random_state=my_seed)\n",
      "clf_most.fit(X_train, y_train)\n",
      "y_pred = clf_most.predict(X_test)\n",
      "cm = confusion_matrix(y_test, y_pred, labels)\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "good/good: 94.20% (65/69)\n",
        "good/bad: 5.80% (4/69)\n",
        "bad/good: 2.33% (1/43)\n",
        "bad/bad: 97.67% (42/43)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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goKAApqamwlHQkuTk5CA0NBSJiYmoqanB4MGDsW7dOomzgNXX1+PLL7/EN998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\nkoiISAYDkoiISMb/A6QhhCw6uuxDAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117b4ee90>"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Going one step further\n",
      "<font size=4>\n",
      "It's nice to give a prediction on whether the binary is good or bad, but it would be nicer to include why that prediction is given.  Let's explore the rebase_size and rebase_off features from the LC_DYLD_INFO command.  The dyld_info_command contains the file offsets and sizes of the new compressed form of the information dyld needs to load the image.<br/>\n",
      "<br/>\n",
      "Dyld rebases an image whenever dyld loads it at an address different from its preferred address.<br/>\n",
      "<ul>\n",
      "<li>rebase_off is the file offset to rebase info</li>\n",
      "<li>rebase_size is the size of rebase info</li>\n",
      "</ul>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_all.boxplot(column='rebase_off', by='label')\n",
      "plt.xlabel('bad vs. good files')\n",
      "plt.ylabel('rebase_off')\n",
      "plt.title('Comparision of rebase offset')\n",
      "plt.suptitle(\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "<matplotlib.text.Text at 0x115e8f8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6pdzc04/Jk396TjIBCA0BDXmw1soYU+72irZV5umnn1b9+vV1zTXX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117b35d90>"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<font size=4>Not the most useful plot, let's try again, but not show the outliers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_all.boxplot(column='rebase_off', by='label', sym='')\n",
      "plt.xlabel('bad vs. good files')\n",
      "plt.ylabel('rebase_off')\n",
      "plt.title('Comparision of rebase offset')\n",
      "plt.suptitle(\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "<matplotlib.text.Text at 0x115ea0b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1186678d0>"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cond = df_all['label'] == 'good'\n",
      "good_all = df_all[cond]\n",
      "bad_all  = df_all[~cond]\n",
      "bvc = pd.DataFrame(bad_all['rebase_off'].value_counts(), columns=['count'])\n",
      "bvc['perctange'] = bvc/bad_all.shape[0]\n",
      "gvc = pd.DataFrame(good_all['rebase_off'].value_counts(), columns=['count'])\n",
      "gvc['perctange'] = gvc/good_all.shape[0]\n",
      "print 'Most common rebase_off values from malicious set'\n",
      "print bvc.head(5)\n",
      "print ''\n",
      "print 'Most common rebase_off values from clean set'\n",
      "print gvc.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Most common rebase_off values from malicious set\n",
        "         count  perctange\n",
        " 0         125   0.490196\n",
        "-1          81   0.317647\n",
        " 20480      10   0.039216\n",
        " 8192       10   0.039216\n",
        " 319488      4   0.015686\n",
        "\n",
        "[5 rows x 2 columns]\n",
        "\n",
        "Most common rebase_off values from clean set\n",
        "        count  perctange\n",
        " 8192      53   0.173770\n",
        " 12288     49   0.160656\n",
        "-1         29   0.095082\n",
        " 16384     27   0.088525\n",
        " 0         21   0.068852\n",
        "\n",
        "[5 rows x 2 columns]\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bad_all['rebase_off'].hist(alpha=1,label='bad',bins=1000)\n",
      "good_all['rebase_off'].hist(alpha=1,label='good',bins=1000)\n",
      "plt.title('Values for rebase offset')\n",
      "plt.legend()\n",
      "plt.xlim(-50,3000000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "(-50, 3000000)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HqlmzZu79+vfvr4cffrjEr7E8oKgGAAAAUKoiI6tIsgLiz9m+XjzLsjRp0iSl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\neejQoWrWrJmeeOIJvfzyy/r73/+u3Nx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       "text": [
        "<matplotlib.figure.Figure at 0x117b32d10>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Well, that wasn't very helpful, let's not show the long tail on the plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bad_all['rebase_off'].hist(alpha=1,label='bad',bins=2000)\n",
      "good_all['rebase_off'].hist(alpha=1,label='good',bins=2000)\n",
      "plt.legend()\n",
      "plt.title('Values for rebase offset')\n",
      "plt.xlim(-50,80000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "(-50, 80000)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mTFHZsmU1Y8YMj7FPP/20JOkf//iHbrzxRvXr10/NmzdXx44dNWLECJ+vHxUV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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1169c8910>"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_all.boxplot(column='rebase_size', by='label')\n",
      "plt.xlabel('bad vs. good files')\n",
      "plt.ylabel('rebase_size')\n",
      "plt.title('Comparision of rebase size')\n",
      "plt.suptitle(\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "<matplotlib.text.Text at 0x1169b0250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NfzFjTT8AAAAAoDCxph8AAAAAgF8hkn4AEGsUAQAoCM6XQNlD0g8AAAAAQDnF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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115e795d0>"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_all.boxplot(column='rebase_size', by='label', sym='')\n",
      "plt.xlabel('bad vs. good files')\n",
      "plt.ylabel('rebase_size')\n",
      "plt.title('Comparision of rebase size')\n",
      "plt.suptitle(\"\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "<matplotlib.text.Text at 0x1169bbbd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ERLyzQwDgAJJrAAAAwAXFxTk7AgCOoFs4AAAA4IIMQ+JXWcD10C0cAAAAAIBi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\nRERERETkpv8BDNiVKzAJFGMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115e7f710>"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bvc = pd.DataFrame(bad_all['rebase_size'].value_counts(), columns=['count'])\n",
      "bvc['percentange'] = bvc/bad_all.shape[0]\n",
      "gvc = pd.DataFrame(good_all['rebase_size'].value_counts(), columns=['count'])\n",
      "gvc['percentange'] = gvc/good_all.shape[0]\n",
      "print 'Most common rebase_size values from malicious set'\n",
      "print bvc.head(5)\n",
      "print ''\n",
      "print 'Most common rebase_size values from clean set'\n",
      "print gvc.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Most common rebase_size values from malicious set\n",
        "     count  percentange\n",
        " 0     125     0.490196\n",
        "-1      81     0.317647\n",
        " 48      6     0.023529\n",
        " 16      6     0.023529\n",
        " 28      5     0.019608\n",
        "\n",
        "[5 rows x 2 columns]\n",
        "\n",
        "Most common rebase_size values from clean set\n",
        "     count  percentange\n",
        " 8      80     0.262295\n",
        " 16     44     0.144262\n",
        "-1      29     0.095082\n",
        " 0      21     0.068852\n",
        " 24     17     0.055738\n",
        "\n",
        "[5 rows x 2 columns]\n"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(good_all['rebase_off'], good_all['rebase_size'], \n",
      "            s=140, c='#aaaaff', label='Good', alpha=.4)\n",
      "plt.scatter(bad_all['rebase_off'], bad['rebase_size'], \n",
      "            s=40, c='r', label='Bad', alpha=.5)\n",
      "plt.title('rebase_off vs rebase_size')\n",
      "plt.xlabel('rebase_off')\n",
      "plt.ylabel('rebase_size')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "<matplotlib.text.Text at 0x119130550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Cgp7DoUMnUF5eXofZEREREdUsFgeIiIgeIoTAmTOJaN26Y5Wx9vaOMDd3QGpq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EyZMkUyMzNl8eLFkpCQIEopCQsLk4KCArP6wsJC6dy5s5hMJklNTZXly5dLTEyMGAwGyc7Otui/\ncOFCUUpJbGysLF++XGbPni2urq4SHh4u5eXluu1NN2b8+PGilJIHH3xQsrKyZNKkSeLo6CixsbEt\nfWh25bnnnpMBAwbIzJkzZdmyZbJgwQKJjY0VpZQMGDDA7H8Gjh49Kl5eXuLj4yPz58+XN954Q3r1\n6iWOjo7yySefWPRuTMZ66t0a5eTkyJw5c2TOnDnSsWNH8fT01G6vWbPGrFZPWeilt61raP7FxcXi\n4eEho0aNkr/85S+yYsUKmTx5srRr104cHR1l48aNFr31khHzt+7KCzlBQUGSk5Mja9asMds+/fRT\nrdZW82H2dWto/jz3W1/+CQkJ0r9/f5k5c6a8+eabsnjxYhk+fLi0bdtW/Pz85Pjx41qtrebT3Nlz\nOPC//vGPf8jgwYMlMDBQXFxcxNnZWcLDw2XGjBlSUlJi9T6nT5+W4cOHi7e3tzg7O0ufPn1kw4YN\ndf6M1atXyy233CLOzs7i4+Mjo0ePlnPnzum+NzVdTU2NLFy4ULp27SpOTk7SqVMnmTx5ssXQhn5f\nmzdvlj/+8Y8SEBAgzs7O4urqKr1795Z58+ZJVVWVRf2PP/4oQ4YMEQ8PDzEajXL77bfLrl27rPZu\nbMZ66d0axcTEiFJKlFJiMBjEYDBot639o6iXLPTU25Y1NP+qqip56qmnpEePHuLp6SmOjo7i7+8v\niYmJcvDgQau99ZQR87eUlJRkkfnV27Xnv63mw+yta2j+PPdbX/4bNmyQ+Ph4uemmm8TZ2VlcXFwk\nIiJCUlJS5OzZsxb1tppPc2avRK5zSX8iIiIiIiIiatV4QUIiIiIiIiIiO8fhABEREREREZGd43CA\niIiIiIiIyM5xOEBERERERERk5zgcICIiIiIiIrJzHA4QERERERER2TkOB4iIiIiIiIjsHIcDRERE\nRERERHaOwwEiIiIiIiIiO8fhABERkZ04efIkDAYD0tPTW/pQWszSpUvRrVs3ODs7w2Aw4JdffgEA\n7N69G/3794e7uzsMBgNycnJa+EiJiIiaV5uWPgAiIiJqXkqplj6EFrF79248++yzSEhIwIwZM+Do\n6Ahvb28UFxfjwQcfRGBgIBYtWgSj0YgBAwa09OESERE1Kw4HiIiIyC58+umnAIDs7Gx4eHho6198\n8QVKSkqQnp6OhISEljo8IiKiFsWPFRAREdmompoaVFRUtPRh2Ixff/0VAMwGA1eve3p6NvsxERER\n6QWHA0RERDZg9erVMBgM2LVrF+bMmYPQ0FC4uLjgvffeAwAsW7YMffr0gaurK0wmE+Li4vDZZ59Z\n7SUiWL9+PXr27AkXFxcEBQUhPT0dNTU1ZnVHjhzBM888g4iICLi7u8PV1RVRUVFYtWqVRc+ioiI8\n//zz2nF5e3sjKioKr776qkXtX//6V9x2221az/79++P9999v8mOzadMm3HrrrXBzc4PJZMJtt92G\nLVu2aPuvXGth9erVAACDwQCDwYDY2FgEBwcjKSkJABAbG6vtIyIisjf8WAEREZENSUlJQXV1NZKT\nk+Hu7o4uXbrgiSeewLvvvovExESMHj0alZWVWLduHQYNGoQPPvgA999/v1mPLVu24Pjx43j22Wfh\n6+uLzZs3Iz09HadOnUJ2drZWt2fPHuzduxeDBw9GcHAwysvLsWHDBowZMwbnzp3D9OnTtdrExETs\n3bsXY8eORc+ePVFRUYHDhw9jz549SElJ0epeeOEFzJ07F/fccw/+/Oc/w2Aw4IMPPkBiYiIyMzPx\nzDPPNOrxWLp0KZ599ll0794dqampEBGsXr0aCQkJWL58OcaMGYOOHTtizZo1WLFiBfbu3Yu1a9cC\nAHx8fFBeXo4dO3ZgxYoVmDVrFrp3796UWIiIiGyfEBERke699dZbopSSbt26SUVFhbb+wQcfiFJK\nVq5caVZfXV0tUVFREhwcrK2dOHFClFLSpk0b+fbbb83qH3jgAVFKyf79+7W18vJyi+Oora2VmJgY\nadeunVy+fFlERM6fPy9KKRk3bly9v8PXX38tSimZNWuWxb6EhARxd3eX0tLSentcraioSFxdXSUs\nLMzsfhcuXJDQ0FAxmUxy/vx5bX3kyJGilLLoc+Wx3bNnT4N/NhERUWvD980RERHZkLFjx8LZ2Vm7\nvXbtWphMJgwePBgFBQXaVlxcjPj4eJw8eRI///yzWY9BgwahV69eZmtTp04FAHz44YfamtFo1P67\nsrIShYWFKCwsxKBBg3DhwgXk5uYCAFxcXODk5IT9+/fj1KlTdR77unXroJTCiBEjzI61oKAA999/\nP0pLS7Fv374GPxaffvopLl68iAkTJsDNzU1bN5lMmDBhAsrKyrBz584G9yMiIrJn/FgBERGRDenS\npYvZ7R9//BGlpaXw8fGxWq+UwtmzZxEWFqatWXvr/JW1EydOaGtlZWVIS0vDhg0bkJeXZ3Gf4uJi\nAEDbtm2xePFiTJw4EcHBwQgPD0dcXBwSEhIQFxdndqwigm7dutV7rA115VgjIiIs9oWHh1v8PkRE\nRFQ3DgeIiIhsyNWv5gO/XVywQ4cOWL9+fZ33sfbkuSGGDRuG7du3Izk5GXfccQfat28PBwcHbN++\nHRkZGaitrdVqk5OTMWTIEGzfvh179uzBxo0bkZmZiUceeUQ7NhGBUgoff/wxHBwcrP7MK0/qiYiI\nqHlxOEBERGTDwsLCsGPHDkRHR8PV1bVB9zl8+HCdayEhIQCA8+fPY9u2bRg5ciSWLl1qVvvJJ59Y\n7evr64vRo0dj9OjRqK2txfDhw7F+/XqkpKSgT58+6NKlC/72t7/hpptuqvPdA40RGhoKAPjhhx8Q\nGxtb7+9DRERE9eM1B4iIiGzYyJEjUVtbixkzZljd/9///tdibefOnfj222+12yKCV155BQCQkJAA\nAHBwcIBSyuzdAQBw5swZrFy5Ekopba2iogIXL140qzMYDOjRoweA377mEACGDx8OAJg5c6ZF37qO\ntT6DBg2Cq6srlixZgrKyMm29tLQUS5YsgclkwqBBg8zuc/VxExER0f/hOweIiIhs2NChQzFq1Chk\nZmbim2++wX333Qdvb2/k5eVh3759OHbsGI4dO2Z2n549eyIuLg7jxo3Tvspw165dGDFiBKKjowH8\ndlG/u+66C2vXroWLiwuioqJw6tQprFixAiEhITh06JDWLzc3FwMHDsSDDz6IiIgIeHp64scff8Sb\nb76JkJAQ3H777QCAqKgopKWlIS0tDb169UJiYiL8/Pxw5swZfP311/joo49QVVXV4N+9Xbt2eOWV\nVzBu3DhER0cjKSlJ+yrD48ePY/ny5TCZTGb3EZGmPtREREStGocDRERENqKuV71XrVqF2NhYrFix\nAvPnz8elS5fg5+eHP/zhD5g/f75F/ZAhQ9ClSxfMmzcPubm58PHxwezZs/Hiiy+a1a1duxbTp0/H\n1q1b8fbbb6NLly6YO3cu2rRpgyeffFKrCwwMxOjRo7F7925s2rQJVVVV6NSpE55++mlMmzbN7NsV\nZs+ejaioKLz++utYvHgxysvL4ePjg8jISCxZsqTRj8nYsWPh5+eHBQsWID09HQDQq1cvfPjhhxg8\neLDF41fXY8h3FBARkb1TwhE6ERERERERkV3jNQeIiIiIiIiI7Bw/VkBERES6UlRUhEuXLtVbYzQa\n4e7u3kxHRERE1PrxYwVERESkKzExMfj888/rrUlKSkJ2dnYzHREREVHrx+EAERER6co333yD8+fP\n11vj7++Pbt26NdMRERERtX4cDhARERERERHZOV6QkIiIiIiIiMjOcThAREREREREZOc4HCAiIiIi\nIiKycxwOEBEREREREdm5/wFRMQGElmdSqAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1169c8190>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Let's zoom in around the origin"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(good_all['rebase_off'], good_all['rebase_size'], \n",
      "            s=140, c='#aaaaff', label='Good', alpha=.4)\n",
      "plt.scatter(bad_all['rebase_off'], bad['rebase_size'], \n",
      "            s=40, c='r', label='Bad', alpha=.5)\n",
      "plt.xlim(-1000,150000)\n",
      "plt.ylim(-100,1100)\n",
      "plt.title('rebase_off vs rebase_size')\n",
      "plt.xlabel('rebase_off')\n",
      "plt.ylabel('rebase_size')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "<matplotlib.text.Text at 0x118632e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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d6MzMTACAm5sbAKC4uBj79++HsbEx9uzZo/UaXY/0qkiShAEDBqgfmb1//z4++eQTLF++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88e3nn+ea9usP1Qa2srvr5B6PU6WltP1Ibi0KEi8vJGcuhQxSm91pnS1NRKSIhnoysCAoLRNIPH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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11910c4d0>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(good_all['rebase_off'], good_all['rebase_size'], \n",
      "            s=140, c='#aaaaff', label='Good', alpha=.4)\n",
      "plt.scatter(bad_all['rebase_off'], bad['rebase_size'], \n",
      "            s=40, c='r', label='Bad', alpha=.5)\n",
      "plt.xlim(-10,10)\n",
      "plt.ylim(-10,10)\n",
      "plt.title('rebase_off vs rebase_size')\n",
      "plt.xlabel('rebase_off')\n",
      "plt.ylabel('rebase_size')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###How does current AV do on these files?\n",
      "<font size=4>\n",
      "Scan data is as of 2014/04/28"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_vt_data(vt_list):\n",
      "    import json\n",
      "    import collections\n",
      "    results = {}\n",
      "    for filename in vt_list:\n",
      "        with open(filename,'rb') as f:\n",
      "            vt_data = f.read()\n",
      "            if len(vt_data) == 0:\n",
      "                break\n",
      "            vt = json.loads(vt_data)\n",
      "            for engine in vt['scans'].keys():\n",
      "                if engine not in results:\n",
      "                    results[engine] = 0\n",
      "                if vt['scans'][engine]['detected']:                \n",
      "                    results[engine] += 1\n",
      "                \n",
      "    r = []\n",
      "    for key, value in results.iteritems():\n",
      "        r.append({'Engine': key, 'Count': value})\n",
      "    return r"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vt_list = glob.glob('data/bad/vt_data/*.vtdata')\n",
      "num_files = len(vt_list)\n",
      "vt_results = load_vt_data(vt_list)\n",
      "vt_df = pd.DataFrame.from_records(vt_results, columns=['Engine', 'Count'])\n",
      "vt_df['Files Scanned'] = num_files\n",
      "vt_df['Percentage'] = vt_df['Count']/num_files\n",
      "vt_df.sort('Count', ascending=0).head(52)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Engine</th>\n",
        "      <th>Count</th>\n",
        "      <th>Files Scanned</th>\n",
        "      <th>Percentage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>                Avast</td>\n",
        "      <td> 261</td>\n",
        "      <td> 261</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td>                GData</td>\n",
        "      <td> 261</td>\n",
        "      <td> 261</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>             F-Secure</td>\n",
        "      <td> 261</td>\n",
        "      <td> 261</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>             Ad-Aware</td>\n",
        "      <td> 260</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.996169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>     MicroWorld-eScan</td>\n",
        "      <td> 260</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.996169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>          BitDefender</td>\n",
        "      <td> 260</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.996169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>            Kaspersky</td>\n",
        "      <td> 255</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.977011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>             Emsisoft</td>\n",
        "      <td> 254</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.973180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>             nProtect</td>\n",
        "      <td> 254</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.973180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>           ESET-NOD32</td>\n",
        "      <td> 254</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.973180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>              AntiVir</td>\n",
        "      <td> 245</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.938697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>               Comodo</td>\n",
        "      <td> 242</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.927203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>               Sophos</td>\n",
        "      <td> 240</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.919540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>                DrWeb</td>\n",
        "      <td> 240</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.919540</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>               Ikarus</td>\n",
        "      <td> 238</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.911877</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>               ClamAV</td>\n",
        "      <td> 231</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.885057</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>            Qihoo-360</td>\n",
        "      <td> 230</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.881226</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td>                  AVG</td>\n",
        "      <td> 228</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.873563</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>        CAT-QuickHeal</td>\n",
        "      <td> 226</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.865900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>       NANO-Antivirus</td>\n",
        "      <td> 224</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.858238</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> TrendMicro-HouseCall</td>\n",
        "      <td> 203</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.777778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>             Symantec</td>\n",
        "      <td> 190</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.727969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td>            Microsoft</td>\n",
        "      <td> 175</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.670498</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td>            AhnLab-V3</td>\n",
        "      <td> 170</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.651341</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>                 Bkav</td>\n",
        "      <td> 149</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.570881</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>                VIPRE</td>\n",
        "      <td> 128</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.490421</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>           TrendMicro</td>\n",
        "      <td> 102</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.390805</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>               McAfee</td>\n",
        "      <td>  90</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.344828</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>    McAfee-GW-Edition</td>\n",
        "      <td>  90</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.344828</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>              ViRobot</td>\n",
        "      <td>  85</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.325670</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>               Norman</td>\n",
        "      <td>  55</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.210728</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td>               Rising</td>\n",
        "      <td>  49</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.187739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>               F-Prot</td>\n",
        "      <td>  42</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.160920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td>            Commtouch</td>\n",
        "      <td>  41</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.157088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>             Jiangmin</td>\n",
        "      <td>  40</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.153257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>          K7AntiVirus</td>\n",
        "      <td>  36</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.137931</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>                 K7GW</td>\n",
        "      <td>  35</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.134100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>         TotalDefense</td>\n",
        "      <td>  34</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.130268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>             Fortinet</td>\n",
        "      <td>  27</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.103448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>              Agnitum</td>\n",
        "      <td>  26</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.099617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td>                VBA32</td>\n",
        "      <td>  25</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.095785</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>               Zillya</td>\n",
        "      <td>  21</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.080460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>                Panda</td>\n",
        "      <td>  11</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.042146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>                  CMC</td>\n",
        "      <td>   5</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.019157</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td>            Antiy-AVL</td>\n",
        "      <td>   1</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.003831</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>             ByteHero</td>\n",
        "      <td>   0</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>  Baidu-International</td>\n",
        "      <td>   0</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>         Malwarebytes</td>\n",
        "      <td>   0</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>            TheHacker</td>\n",
        "      <td>   0</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>             AegisLab</td>\n",
        "      <td>   0</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>             Kingsoft</td>\n",
        "      <td>   0</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>     SUPERAntiSpyware</td>\n",
        "      <td>   0</td>\n",
        "      <td> 261</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>52 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "                  Engine  Count  Files Scanned  Percentage\n",
        "18                 Avast    261            261    1.000000\n",
        "40                 GData    261            261    1.000000\n",
        "28              F-Secure    261            261    1.000000\n",
        "25              Ad-Aware    260            261    0.996169\n",
        "1       MicroWorld-eScan    260            261    0.996169\n",
        "21           BitDefender    260            261    0.996169\n",
        "20             Kaspersky    255            261    0.977011\n",
        "34              Emsisoft    254            261    0.973180\n",
        "2               nProtect    254            261    0.973180\n",
        "45            ESET-NOD32    254            261    0.973180\n",
        "31               AntiVir    245            261    0.938697\n",
        "27                Comodo    242            261    0.927203\n",
        "26                Sophos    240            261    0.919540\n",
        "29                 DrWeb    240            261    0.919540\n",
        "47                Ikarus    238            261    0.911877\n",
        "19                ClamAV    231            261    0.885057\n",
        "51             Qihoo-360    230            261    0.881226\n",
        "49                   AVG    228            261    0.873563\n",
        "4          CAT-QuickHeal    226            261    0.865900\n",
        "12        NANO-Antivirus    224            261    0.858238\n",
        "17  TrendMicro-HouseCall    203            261    0.777778\n",
        "14              Symantec    190            261    0.727969\n",
        "38             Microsoft    175            261    0.670498\n",
        "42             AhnLab-V3    170            261    0.651341\n",
        "0                   Bkav    149            261    0.570881\n",
        "30                 VIPRE    128            261    0.490421\n",
        "32            TrendMicro    102            261    0.390805\n",
        "5                 McAfee     90            261    0.344828\n",
        "33     McAfee-GW-Edition     90            261    0.344828\n",
        "23               ViRobot     85            261    0.325670\n",
        "15                Norman     55            261    0.210728\n",
        "46                Rising     49            261    0.187739\n",
        "13                F-Prot     42            261    0.160920\n",
        "41             Commtouch     41            261    0.157088\n",
        "35              Jiangmin     40            261    0.153257\n",
        "9            K7AntiVirus     36            261    0.137931\n",
        "10                  K7GW     35            261    0.134100\n",
        "16          TotalDefense     34            261    0.130268\n",
        "48              Fortinet     27            261    0.103448\n",
        "22               Agnitum     26            261    0.099617\n",
        "43                 VBA32     25            261    0.095785\n",
        "7                 Zillya     21            261    0.080460\n",
        "50                 Panda     11            261    0.042146\n",
        "3                    CMC      5            261    0.019157\n",
        "36             Antiy-AVL      1            261    0.003831\n",
        "24              ByteHero      0            261    0.000000\n",
        "44   Baidu-International      0            261    0.000000\n",
        "6           Malwarebytes      0            261    0.000000\n",
        "11             TheHacker      0            261    0.000000\n",
        "39              AegisLab      0            261    0.000000\n",
        "37              Kingsoft      0            261    0.000000\n",
        "8       SUPERAntiSpyware      0            261    0.000000\n",
        "\n",
        "[52 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vt_list = glob.glob('data/good/vt_data/*.vtdata')\n",
      "num_files = len(vt_list)\n",
      "vt_results = load_vt_data(vt_list)\n",
      "vt_df = pd.DataFrame.from_records(vt_results, columns=['Engine', 'Count'])\n",
      "vt_df['Files Scanned'] = num_files\n",
      "vt_df['Percentage'] = vt_df['Count']/num_files\n",
      "vt_df.sort('Count', ascending=0).head(15)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Engine</th>\n",
        "      <th>Count</th>\n",
        "      <th>Files Scanned</th>\n",
        "      <th>Percentage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>            Qihoo-360</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>                Panda</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>     SUPERAntiSpyware</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>       NANO-Antivirus</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>          BitDefender</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>            Kaspersky</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>               ClamAV</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>                Avast</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> TrendMicro-HouseCall</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>         TotalDefense</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>               Norman</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>             Symantec</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>               F-Prot</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>              Agnitum</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>          K7AntiVirus</td>\n",
        "      <td> 0</td>\n",
        "      <td> 266</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>15 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "                  Engine  Count  Files Scanned  Percentage\n",
        "51             Qihoo-360      0            266           0\n",
        "50                 Panda      0            266           0\n",
        "23      SUPERAntiSpyware      0            266           0\n",
        "22        NANO-Antivirus      0            266           0\n",
        "21           BitDefender      0            266           0\n",
        "20             Kaspersky      0            266           0\n",
        "19                ClamAV      0            266           0\n",
        "18                 Avast      0            266           0\n",
        "17  TrendMicro-HouseCall      0            266           0\n",
        "16          TotalDefense      0            266           0\n",
        "15                Norman      0            266           0\n",
        "14              Symantec      0            266           0\n",
        "13                F-Prot      0            266           0\n",
        "12               Agnitum      0            266           0\n",
        "11           K7AntiVirus      0            266           0\n",
        "\n",
        "[15 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 101
    }
   ],
   "metadata": {}
  }
 ]
}