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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## SQL Injection Exercise\n",
      "\n",
      "<div style=\"float: right;\"><img src=\"files/images/sql_injection.gif\" width=\"300px\"></div>\n",
      "<font size=4>\"SQL injection is a code injection technique, used to attack data driven applications, in which malicious SQL statements areA SQL injection attack consists of insertion or \"injection\" of a SQL query via the input data from the client to the application. A successful SQL injection exploit can read sensitive data from the database, modify database data (Insert/Update/Delete), execute administration operations on the database (such as shutdown the DBMS), recover the content of a given file present on the DBMS file system and in some cases issue commands to the operating system. SQL injection attacks are a type of injection attack, in which SQL commands are injected into data-plane input in order to effect the execution of predefined SQL commands.\" -OWASP\n",
      "<br>\n",
      "<br><br>\n",
      "** All Code and IPython Notebooks for this talk http://clicksecurity.github.io/data_hacking **\n",
      "<br><br>\n",
      "Tools:\n",
      "<ul>\n",
      "  <li>sqlmap: Automatic SQL injection and database takeover tool (http://sqlmap.org)\n",
      "  <li>JBroFuzz (https://www.owasp.org/index.php/JBroFuzz)\n",
      "  <li>sqlparse (https://github.com/andialbrecht/sqlparse)\n",
      "  <br>\n",
      "  <br><br>\n",
      "  <li> IPython: A mad scientist notebook! (http://ipython.org)\n",
      "  <ul><li> What did you do?\n",
      "  <li> How did you do it?\n",
      "  <li> Can I repoduce it?\n",
      "  <li> Easy to share:\n",
      "  <ul>\n",
      "  <li>[NB Viewer](http://nbviewer.ipython.org)\n",
      "  <li>[Reddit IPython](http://www.reddit.com/r/ipython)\n",
      "  </ul>\n",
      "  </ul>\n",
      "  <br>\n",
      "  <li> Pandas: Python Data Analysis Library (http://pandas.pydata.org)\n",
      "  <ul>\n",
      "  <li> A fast and efficient DataFrame object\n",
      "  <li> Great set of IO Tools\n",
      "  <li> Fantastic handling of missing data\n",
      "  <li> Flexible reshaping and pivoting\n",
      "  <li> Slicing, indexing, and subsetting\n",
      "  </ul>\n",
      "</ul>\n",
      "\n",
      "Approach:\n",
      "<ul>\n",
      "  <li> Gather data (Thanks to Ray VanHoose for his assistance!)\n",
      "  <li> Exploration and Understanding\n",
      "  <li> Some Simple Statistics\n",
      "  <li> Feature Vectors\n",
      "  <li> Random Forest Machine Learning\n",
      "</ul>\n",
      "</font>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sklearn.feature_extraction\n",
      "sklearn.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "'0.14.1'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "pd.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "'0.13.1'"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting defaults\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "plt.rcParams['font.size'] = 18.0\n",
      "plt.rcParams['figure.figsize'] = 12.0, 5.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# A plotting helper method\n",
      "def plot_it(df,label_x,label_y):\n",
      "    fig, ax = plt.subplots(subplot_kw={'axisbg':'#EEEEE5'})\n",
      "    ax.grid(color='grey', linestyle='solid')\n",
      "    df.T.plot(kind='bar', logx=True, rot=0, ax=ax, colormap='PiYG')\n",
      "    ax.legend(loc=0, prop={'size':14})\n",
      "    plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0)\n",
      "    plt.xlabel(label_x)\n",
      "    plt.ylabel(label_y)\n",
      "    return ax"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Read in a set of SQL statements from various sources\n",
      "import os\n",
      "basedir = 'data'\n",
      "filelist = os.listdir(basedir) \n",
      "df_list = []\n",
      "for file in filelist:\n",
      "    df = pd.read_csv(os.path.join(basedir,file), sep='|||', names=['raw_sql'], header=None)\n",
      "    df['type'] = 'legit' if file.split('.')[0] == 'legit' else 'malicious'\n",
      "    df_list.append(df)\n",
      "dataframe = pd.concat(df_list, ignore_index=True)\n",
      "dataframe.dropna(inplace=True)\n",
      "print dataframe['type'].value_counts()\n",
      "dataframe.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "malicious    12892\n",
        "legit         1003\n",
        "dtype: int64\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>raw_sql</th>\n",
        "      <th>type</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    '; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
        "      <td> malicious</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>          create user name identified by 'pass123'</td>\n",
        "      <td> malicious</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> create user name identified by pass123 tempora...</td>\n",
        "      <td> malicious</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>              exec sp_addlogin 'name' , 'password'</td>\n",
        "      <td> malicious</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>      exec sp_addsrvrolemember 'name' , 'sysadmin'</td>\n",
        "      <td> malicious</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                                             raw_sql       type\n",
        "0     '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious\n",
        "1           create user name identified by 'pass123'  malicious\n",
        "2  create user name identified by pass123 tempora...  malicious\n",
        "3               exec sp_addlogin 'name' , 'password'  malicious\n",
        "4       exec sp_addsrvrolemember 'name' , 'sysadmin'  malicious\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!which python"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/usr/bin/python\r\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use the SQLParse module: sqlparse is a non-validating SQL parser module for Python\n",
      "# https://github.com/andialbrecht/sqlparse\n",
      "import sqlparse\n",
      "def parse_it(raw_sql):\n",
      "    parsed = sqlparse.parse(unicode(raw_sql,'utf-8'))\n",
      "    return [token._get_repr_name() for parse in parsed for token in parse.tokens if token._get_repr_name() != 'Whitespace']\n",
      "\n",
      "dataframe['parsed_sql'] = dataframe['raw_sql'].map(lambda x: parse_it(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataframe.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>raw_sql</th>\n",
        "      <th>type</th>\n",
        "      <th>parsed_sql</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    '; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Single, Identifier, Float, Float, Float, Erro...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>          create user name identified by 'pass123'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>       [DDL, Keyword, Identifier, Keyword, Single]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> create user name identified by pass123 tempora...</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [DDL, Keyword, Identifier, Keyword, Identifier...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>              exec sp_addlogin 'name' , 'password'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>             [Keyword, Identifier, IdentifierList]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>      exec sp_addsrvrolemember 'name' , 'sysadmin'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>             [Keyword, Identifier, IdentifierList]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "                                             raw_sql       type  \\\n",
        "0     '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious   \n",
        "1           create user name identified by 'pass123'  malicious   \n",
        "2  create user name identified by pass123 tempora...  malicious   \n",
        "3               exec sp_addlogin 'name' , 'password'  malicious   \n",
        "4       exec sp_addsrvrolemember 'name' , 'sysadmin'  malicious   \n",
        "\n",
        "                                          parsed_sql  \n",
        "0  [Single, Identifier, Float, Float, Float, Erro...  \n",
        "1        [DDL, Keyword, Identifier, Keyword, Single]  \n",
        "2  [DDL, Keyword, Identifier, Keyword, Identifier...  \n",
        "3              [Keyword, Identifier, IdentifierList]  \n",
        "4              [Keyword, Identifier, IdentifierList]  \n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Looking at the SQL tokens is 'kinda' interesting but sequences of tokens and transitions\n",
      "# between tokens seems more meaningful so we're also going to compute sequences by\n",
      "# computing NGrams for every SQL statement...\n",
      "def ngrams(lst, N):\n",
      "    ngrams = []\n",
      "    for n in xrange(0,N):\n",
      "        ngrams += zip(*(lst[i:] for i in xrange(n+1)))\n",
      "    return [str(tuple) for tuple in ngrams]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataframe['sequences'] = dataframe['parsed_sql'].map(lambda x: ngrams(x, 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### We'd like to run some simple statistics to see what correlations the data might contain. Here we want to see if certain tokens or sets of transitions are indicative of malicious sql statements.\n",
      "\n",
      "#### G-test is for goodness of fit to a distribution and for independence in contingency tables. It's related to chi-squared, multinomial and Fisher's exact test, please see http://en.wikipedia.org/wiki/G_test."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Helper method\n",
      "def token_expansion(series, types):\n",
      "    _tokens, _types = zip(*[(token,token_type) for t_list,token_type in zip(series,types) for token in t_list])\n",
      "    return pd.Series(_tokens), pd.Series(_types)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataframe['sequences']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "0     [('Single',), ('Identifier',), ('Float',), ('F...\n",
        "1     [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
        "2     [('DDL',), ('Keyword',), ('Identifier',), ('Ke...\n",
        "3     [('Keyword',), ('Identifier',), ('IdentifierLi...\n",
        "4     [('Keyword',), ('Identifier',), ('IdentifierLi...\n",
        "5     [('DML',), ('Keyword',), ('Identifier',), ('Ke...\n",
        "6     [('Keyword',), ('Keyword',), ('Keyword',), ('I...\n",
        "7     [('DML',), ('Keyword',), ('Function',), ('Keyw...\n",
        "8     [('Integer',), ('Error',), ('Integer',), ('Int...\n",
        "9     [('Integer',), ('Keyword',), ('Comparison',), ...\n",
        "10    [('Integer',), ('Single',), ('Integer',), ('Si...\n",
        "11    [('Integer',), ('Keyword',), ('Function',), ('...\n",
        "12    [('Error',), ('Error',), ('Punctuation',), ('O...\n",
        "13    [('Integer',), ('Error',), ('Error',), ('Integ...\n",
        "14    [('Integer',), ('Single',), ('Integer',), ('In...\n",
        "...\n",
        "13881    [('DML',), ('Identifier',), ('Keyword',), ('Co...\n",
        "13882    [('DML',), ('Identifier',), ('Keyword',), ('Co...\n",
        "13883    [('DML',), ('Identifier',), ('Keyword',), ('Co...\n",
        "13884    [('DML',), ('Identifier',), ('Keyword',), ('Co...\n",
        "13885    [('DML',), ('Identifier',), ('Keyword',), ('Id...\n",
        "13886    [('DML',), ('Identifier',), ('Keyword',), ('Co...\n",
        "13887    [('Identifier',), ('Builtin',), ('Identifier',...\n",
        "13888    [('Identifier',), ('Function',), ('Identifier'...\n",
        "13889    [('Identifier',), ('Function',), ('Identifier'...\n",
        "13890    [('Identifier',), ('Function',), ('Identifier'...\n",
        "13891    [('Identifier',), ('Function',), ('Identifier'...\n",
        "13892    [('Identifier',), ('Function',), ('Identifier'...\n",
        "13893    [('Identifier',), ('Function',), ('Identifier'...\n",
        "13894    [('Keyword',), ('Identifier',), ('Function',),...\n",
        "13895    [('Keyword',), ('IdentifierList',), ('DML',), ...\n",
        "Name: sequences, Length: 13895, dtype: object"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The data hacking repository has a simple stats module we're going to use\n",
      "import data_hacking.simple_stats as ss\n",
      "\n",
      "# Spin up our g_test class\n",
      "g_test = ss.GTest()\n",
      "\n",
      "# Here we'd like to see how various sql tokens and transitions are related.\n",
      "# Is there an association with particular token sets and malicious SQL statements.\n",
      "tokens, types = token_expansion(dataframe['sequences'], dataframe['type'])\n",
      "df_ct, df_cd, df_stats = g_test.highest_gtest_scores(tokens, types, matches=0, N=0)\n",
      "\n",
      "df_stats.sort('malicious_g', ascending=0).head(10)\n",
      "\n",
      "# The table below shows raw counts, conditional distributions, expected counts, and g-test score."
     ],
     "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>legit</th>\n",
        "      <th>malicious</th>\n",
        "      <th>legit_cd</th>\n",
        "      <th>malicious_cd</th>\n",
        "      <th>total_cd</th>\n",
        "      <th>legit_exp</th>\n",
        "      <th>legit_g</th>\n",
        "      <th>malicious_exp</th>\n",
        "      <th>malicious_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>('Single',)</th>\n",
        "      <td>    7</td>\n",
        "      <td> 10984</td>\n",
        "      <td> 0.000637</td>\n",
        "      <td> 0.999363</td>\n",
        "      <td> 10991</td>\n",
        "      <td> 1121.927951</td>\n",
        "      <td> -71.076512</td>\n",
        "      <td>  9869.072049</td>\n",
        "      <td> 2351.319327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Single', 'Identifier')</th>\n",
        "      <td>    0</td>\n",
        "      <td>  8309</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>  8309</td>\n",
        "      <td>  848.157524</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  7460.842476</td>\n",
        "      <td> 1789.275422</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Punctuation',)</th>\n",
        "      <td>  152</td>\n",
        "      <td>  7707</td>\n",
        "      <td> 0.019341</td>\n",
        "      <td> 0.980659</td>\n",
        "      <td>  7859</td>\n",
        "      <td>  802.222889</td>\n",
        "      <td>-505.705813</td>\n",
        "      <td>  7056.777111</td>\n",
        "      <td> 1358.598582</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Identifier',)</th>\n",
        "      <td> 1284</td>\n",
        "      <td> 17011</td>\n",
        "      <td> 0.070183</td>\n",
        "      <td> 0.929817</td>\n",
        "      <td> 18295</td>\n",
        "      <td> 1867.498123</td>\n",
        "      <td>-962.022691</td>\n",
        "      <td> 16427.501877</td>\n",
        "      <td> 1187.480739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Identifier', 'Single')</th>\n",
        "      <td>    2</td>\n",
        "      <td>  4222</td>\n",
        "      <td> 0.000473</td>\n",
        "      <td> 0.999527</td>\n",
        "      <td>  4224</td>\n",
        "      <td>  431.173111</td>\n",
        "      <td> -21.493450</td>\n",
        "      <td>  3792.826889</td>\n",
        "      <td>  905.174233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Single', 'Identifier', 'Single')</th>\n",
        "      <td>    0</td>\n",
        "      <td>  4170</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>  4170</td>\n",
        "      <td>  425.660955</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  3744.339045</td>\n",
        "      <td>  897.975510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Identifier', 'Single', 'Identifier')</th>\n",
        "      <td>    0</td>\n",
        "      <td>  4162</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>  4162</td>\n",
        "      <td>  424.844339</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  3737.155661</td>\n",
        "      <td>  896.252775</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Identifier', 'Identifier')</th>\n",
        "      <td>    4</td>\n",
        "      <td>  3957</td>\n",
        "      <td> 0.001010</td>\n",
        "      <td> 0.998990</td>\n",
        "      <td>  3961</td>\n",
        "      <td>  404.326869</td>\n",
        "      <td> -36.927434</td>\n",
        "      <td>  3556.673131</td>\n",
        "      <td>  844.111737</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Keyword', 'Keyword', 'DML')</th>\n",
        "      <td>   18</td>\n",
        "      <td>  3248</td>\n",
        "      <td> 0.005511</td>\n",
        "      <td> 0.994489</td>\n",
        "      <td>  3266</td>\n",
        "      <td>  333.383376</td>\n",
        "      <td>-105.081169</td>\n",
        "      <td>  2932.616624</td>\n",
        "      <td>  663.529712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>('Keyword', 'DML', 'IdentifierList')</th>\n",
        "      <td>   28</td>\n",
        "      <td>  3157</td>\n",
        "      <td> 0.008791</td>\n",
        "      <td> 0.991209</td>\n",
        "      <td>  3185</td>\n",
        "      <td>  325.115142</td>\n",
        "      <td>-137.310594</td>\n",
        "      <td>  2859.884858</td>\n",
        "      <td>  624.081095</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                                        legit  malicious  legit_cd  \\\n",
        "('Single',)                                 7      10984  0.000637   \n",
        "('Single', 'Identifier')                    0       8309  0.000000   \n",
        "('Punctuation',)                          152       7707  0.019341   \n",
        "('Identifier',)                          1284      17011  0.070183   \n",
        "('Identifier', 'Single')                    2       4222  0.000473   \n",
        "('Single', 'Identifier', 'Single')          0       4170  0.000000   \n",
        "('Identifier', 'Single', 'Identifier')      0       4162  0.000000   \n",
        "('Identifier', 'Identifier')                4       3957  0.001010   \n",
        "('Keyword', 'Keyword', 'DML')              18       3248  0.005511   \n",
        "('Keyword', 'DML', 'IdentifierList')       28       3157  0.008791   \n",
        "\n",
        "                                        malicious_cd  total_cd    legit_exp  \\\n",
        "('Single',)                                 0.999363     10991  1121.927951   \n",
        "('Single', 'Identifier')                    1.000000      8309   848.157524   \n",
        "('Punctuation',)                            0.980659      7859   802.222889   \n",
        "('Identifier',)                             0.929817     18295  1867.498123   \n",
        "('Identifier', 'Single')                    0.999527      4224   431.173111   \n",
        "('Single', 'Identifier', 'Single')          1.000000      4170   425.660955   \n",
        "('Identifier', 'Single', 'Identifier')      1.000000      4162   424.844339   \n",
        "('Identifier', 'Identifier')                0.998990      3961   404.326869   \n",
        "('Keyword', 'Keyword', 'DML')               0.994489      3266   333.383376   \n",
        "('Keyword', 'DML', 'IdentifierList')        0.991209      3185   325.115142   \n",
        "\n",
        "                                           legit_g  malicious_exp  malicious_g  \n",
        "('Single',)                             -71.076512    9869.072049  2351.319327  \n",
        "('Single', 'Identifier')                  0.000000    7460.842476  1789.275422  \n",
        "('Punctuation',)                       -505.705813    7056.777111  1358.598582  \n",
        "('Identifier',)                        -962.022691   16427.501877  1187.480739  \n",
        "('Identifier', 'Single')                -21.493450    3792.826889   905.174233  \n",
        "('Single', 'Identifier', 'Single')        0.000000    3744.339045   897.975510  \n",
        "('Identifier', 'Single', 'Identifier')    0.000000    3737.155661   896.252775  \n",
        "('Identifier', 'Identifier')            -36.927434    3556.673131   844.111737  \n",
        "('Keyword', 'Keyword', 'DML')          -105.081169    2932.616624   663.529712  \n",
        "('Keyword', 'DML', 'IdentifierList')   -137.310594    2859.884858   624.081095  \n",
        "\n",
        "[10 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now plot the the head() and the tail() of the dataframe to see who's been naughty or nice\n",
      "sorted_df = df_stats.sort('malicious_g', ascending=0)\n",
      "naughty = sorted_df.head(7)\n",
      "nice = sorted_df.tail(7).sort('malicious_g', ascending=0)\n",
      "naughty_and_nice = pd.concat([naughty, nice])\n",
      "ax = plot_it(naughty_and_nice[['malicious_g']],'SQL Command Types','G-Test Scores')\n",
      "ax.set_xlim(.2, 1.4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(0.2, 1.4)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7d/Lcc89x33330bNnT7vPtzTXrl3j0KFDdO/e3aHjikDVnRkeUvAqqyNAv5tG\nmeQBbxW8HD22iIiISLXm7elF8Prie57e3uOVVXh4OO7u7mzevJnw8HCrMTfb/Xjx4sX88Y9/pFOn\nTgQGBhIXF1eikLvZGE2aNCE5OZno6Gh69uyJwWAgODiYf/7znwAMHz6cpKQkOnbsyNWrV9myZQvd\nu3e/6bGfeOIJoqOjGTt2LNnZ2fTp04cZM2bwyiuvmGMGDBhAYmIiDz30EBcvXmTp0qW8+OKLJXJc\nu3Ytf/rTn8zF78MPP8w//vEPi3O0dp6lnfvSpUsZOnQo6enp5vuR//3vf9OsWTO7dswWKaubP+la\nHC3//Hmrm0w7hdHYlLcMTzs7DavG5ydSma6ViJikpOzSBloiclNGY9Nyz7A609SpUzl16hQrVmgT\nz4o2bdo0EhMT2bt3r/ke6CeffJLu3bsTHR3t5OykMjMYDDbrBqOxKdioe6vqzLCIiIiIiMNFR0fT\npk0b0tLSLB5nJLff559/TlxcnLkQ3rdvH3v27GH16tVOzkzuVCqGRUREREQKeHl5kZmZ6ew0qqXU\n1FSLz8HBwXz33XdOykaqg6q6gZaIiIiIiIhIuakYFhERERERkWpHxbCIiIiIiIhUO7pnWCotVwyF\nu79VKl51PTl1+rCz0xARERERkVugYlgqrVzy+bz+eGenUcKjWWV9BLWIiIiIiFRWWiYtIiIiIiIi\n1Y6KYREREREREal2VAyLiIiISIUx1vPGYDBU2MtYz9uu/C5dukSjRo1IS0sr1/nt3LkTFxcXTp8+\nXa7+9khKSsLFxYXz58+b29auXUurVq1wc3Nj6NChbN26tUTM7TJ+/HjGjRt3248j4ii6Z1hERERE\nKsyFSxf5PnZThR3PNzrcrvjY2FjCw8Px9/cnPT0df39/8vLyAFPx2atXL7KysjAajbcjXZv8/PyI\njIxkwoQJ5rYuXbqQmZlpkcuwYcMYMWIEkZGR1K1bF3d39xIxt5LDsmXL6NGjh9XvJ06cSOvWrZkw\nYQK+vr63fDyR200zwyIiIiIiwPXr10lISGDIkCHOTqUEg8FQos3NzY2GDRuaP1+4cIHz58/Tu3dv\nGjdujIeHR4mY8sjJyTHnYC2PQo0aNaJ79+68++67t3Q8kYqiYlhEREREBNi0aRPZ2dn06tWrzH02\nbNhA27ZtqVWrFt27d+fYsWMlYrZv306PHj2oU6cOvr6+jB49mitXrpi/DwsL45VXXuHPf/4zDRo0\nwMfHh+h7NiFgAAAgAElEQVToaPLz883fZ2RkEB0djYuLC66uroDlMumkpCTuvvtuAHr16oWLiwvJ\nyclWl1KXJZ/Ro0cTFRVFw4YN6dq1a5mvx1NPPcUHH3xQ5ngRZ1IxLCIiIiICJCcnExISYjH7WdpM\n6HfffUe/fv3o06cPe/fuJTIykokTJ1r02b9/P3369KFfv37s27ePxMRE9uzZw9ChQy3GWrFiBTVq\n1ODrr7/m7bffZv78+Xz44YcAfPLJJ/j6+jJt2jQyMzP58ccfS+TSpUsXDh48CEBiYiKZmZk88MAD\nJeLKms/777+PwWDgq6++Yvny5WW4eiadOnXi+PHjZGZmlrmPiLPonmEREREREeD48eM0b97c/NnP\nz4/c3Fyb8QsXLsTPz48FCxYA0Lp1a44dO8Zrr71mjomNjWXgwIHmjaUCAgKIj48nJCSErKws6tev\nD0BQUBDTp08HIDAwkISEBDZv3sygQYPw9vbG1dUVDw8Pm0ue3dzcaNCgAQBGo9FmXFnz8ff3JzY2\n1qLvqVOnbF6LQoXX7/jx4zRq1Oim8SLOpGJYRERERAS4cuUKPj4+ZY4/fPgwoaGhFm3FP+/atYuT\nJ0+aZ3kB8vPzMRgMnDx5kvr162MwGAgODrbo17hxY3766adynEXpypIPwH333Veu8T09PQHTrtwi\nlZ2KYRERERERwMvLy+Le2ZsxGAzm+3ptyc/PZ/jw4VYfOdSkSRPzz25ubiXGLtzF2pHKko/BYKBO\nnTrlGv/y5csA1KtXr/xJilQQFcMiIiIiIpiWJ2/fvr3M8e3atWPNmjUWbSkpKRafQ0JCOHDgAP7+\n/reUW40aNUpdsl1WjsrHloyMDMB0LUUqO22gJSIiIiICdOvWjd27d990trfQqFGjSE9PZ+zYsRw9\nepSPP/6YRYsWWcRMmjSJ1NRUIiIi2L17NydOnGD9+vWMGjXKHJOfn3/TY/r5+ZGcnMyZM2fIysqy\n/+QcnI8tqamptGrVSvcLS5WgmWERERERqTDeXvXwjQ6v0OOVVXh4OO7u7mzevJnwcOs5Ft0pulmz\nZiQmJjJ+/HgWLVpEx44dmTVrFi+88II5pn379iQnJzN16lTCwsLIzc3F39+fp59+2mLM4rtWF2+b\nMWMGI0eOJCAggOvXr5tnia31Ky3n8uZjTVhYGAaDgS1btpjb1q1bx+DBg2/aV6QyuPm/cnG0/PPn\nf3B2DmZGY1PeMjx980AnGJ+fyOf1xzs7jRIezXqLyvTfUKSipaTsIjS0fBuriEj1YTQ2LffsojNN\nnTqVU6dOsWLFCmenUun5+fkRERHBpEmTAPjxxx9p06YNhw4dwtfX18nZSXViMBhs/n5uNDYFG3Wv\nlkmLiIiIiBSIjo5m8+bNpKWlOTuVSu3gwYO4u7szYcIEc9ucOXP44x//qEJYqgwtkxYRERERKeDl\n5UVmZqaz06j0goKCOHLkiEXb3LlznZSNSPloZlhERERERESqHRXDIiIiIiIiUu2oGBYREREREZFq\nR8WwiIiIiIiIVDsqhkVERERERKTaUTEsIiIiIiIi1Y6KYREREREREal2VAyLiIiISIUxehsxGAwV\n9jJ6G+3O8dKlSzRq1Ii0tLRyn+fOnTtxcXHh9OnT5R6jrJKSknBxceH8+fPmtrVr19KqVSvc3NwY\nOnQoW7duLRFTmVk7J0eYPn067du3L3f/s2fP0qBBAz2L+g5xl7MTEKlqXA0uGI1NnZ2GVfU8PEnL\nOOzsNERERGy6cPEClzccr7DjeT7Syu4+sbGxhIeH4+/vD0B6ejr+/v7k5eUBpkKtV69eZGVlYTTa\nX2zfCj8/PyIjI5kwYYK5rUuXLmRmZlrkMmzYMEaMGEFkZCR169bF3d29RMyt5LBs2TJ69OhR5j4u\nLi6kp6fTvHlzAD799FPefPNNjhw5wo0bN/D19aVr164kJCTYPCdnKXq+Pj4+DB48mJkzZxIXF+fs\n1OQWqRgWsVNufh77nljo7DSsCl4f4ewUREREqrTr16+TkJDAypUrnZ2KVQaDoUSbm5sbDRs2NH++\ncOEC58+fp3fv3jRu3NjcXjSmPHJycnBzczPPupfX5s2befbZZ5kxYwbLli3D1dWVw4cPs3btWnNM\n8XNypuLn++KLL9KzZ09mz55NnTp1nJiZ3CotkxYRERERKbBp0yays7Pp1auXXf02bNhA27ZtqVWr\nFt27d+fYsWMlYrZv306PHj2oU6cOvr6+jB49mitXrpi/DwsL45VXXuHPf/4zDRo0wMfHh+joaPLz\n883fZ2RkEB0djYuLC66uroDlkuKkpCTuvvtuAHr16oWLiwvJyclWlx2XJZ/Ro0cTFRVFw4YN6dq1\nq13XxJbPPvuM0NBQJk+eTOvWrQkICOCJJ54wzwoXPyeApUuX4uHhwX//+1/uvfde6tatS69evUhP\nT7cY+4033sDHxwdPT0+GDh3KjBkzaNmyZan5LFmyhHvuuYdatWrRpk0b5s+fb77m1nTs2BFPT0/W\nrVtX/osglYKKYRERERGRAsnJyYSEhJSY+SxtJvS7776jX79+9OnTh7179xIZGcnEiRMt+uzfv58+\nffrQr18/9u3bR2JiInv27GHo0KEWY61YsYIaNWrw9ddf8/bbbzN//nw+/PBDAD755BN8fX2ZNm0a\nmZmZ/PjjjyVy6dKlCwcPHgQgMTGRzMxMHnjggRJxZc3n/fffx2Aw8NVXX7F8+fKbXD3bil6Lxo0b\nc/jwYfbt22fXGL/99huzZs1i6dKlfP3111y8eJFRo0aZv1+1ahUzZszgjTfe4Ntvv6V169bMmzev\n1P92CQkJxMTE8Prrr3PkyBHmzp3Lm2++SXx8fKm5dO7cma1bt9qVv1Q+WiYtIiIiIlLg+PHj5vta\nC/n5+ZGbm2uzz8KFC/Hz82PBggUAtG7dmmPHjvHaa6+ZY2JjYxk4cCDjxo0DICAggPj4eEJCQsjK\nyqJ+/foABAUFMX36dAACAwNJSEhg8+bNDBo0CG9vb1xdXfHw8LC5hNjNzY0GDRoAYDQabcaVNR9/\nf39iY2Mt+p46dcrmtbCl6PWLjIzkyy+/5Pe//z2+vr7cf//9hIeH8/zzz5e67PjGjRvExcXRqpXp\nPvCoqCiL4n3BggUMGTLE3DZ58mS2bNnC8eO271GfOXMmsbGxPP300wC0aNGCSZMmER8fzyuvvGLz\nfJs1a8aBAwfsuAJSGWlmWERERESkwJUrV6hbt65dfQ4fPkxoaKhFW/HPu3bt4v3338fDw8P86tq1\nKwaDgZMnTwKm2dPg4GCLfo0bN+ann34qx5mUriz5ANx3330OP3bt2rVZv349J06cYNq0adSrV48p\nU6YQFBRU6rnWrFnTXAiD6dpcv36dixcvAnD06FE6d+5s0adz5842lzyfO3eO77//nhEjRlhchylT\nptx0J3FPT08uXbpU1lOWSkozwyIiIiIiBby8vCzumy0Lg8FQ6j2mAPn5+QwfPtw8E1tUkyZNzD+7\nubmVGLtwF2tHKks+BoPhtm4Q5e/vj7+/P8OGDSMmJobWrVuzcOFCpk2bZjX+rrssS5fC5c/lvT6F\n/RYtWsSDDz5oV9/Lly/j7e1druNK5aFiWERERESkQGBgINu3b7erT7t27VizZo1FW0pKisXnkJAQ\nDhw4YH5cU3nVqFGj1CXbZeWofBylRYsW1KpVi6tXr5Z7jLZt25KamsrLL79sbktNTbV5z7CPjw9N\nmjThxIkTPP/883YdKyMjw2KWWqomLZMWERERESnQrVs3du/efdOZ3qJGjRpFeno6Y8eO5ejRo3z8\n8ccsWrTIImbSpEmkpqYSERHB7t27OXHiBOvXr7fYACo/P/+mx/Xz8yM5OZkzZ86QlZVl38ndhnzK\nY/r06UyaNImtW7dy6tQpdu/ezdChQ7l27Rp9+/Yt97hjxoxh6dKlLFmyhOPHjzN79uxSi2GAv/zl\nL8yePZv58+dz9OhRDhw4wPLly5k1a1apx9q5cyfdu3cvd65SOWhmWEREREQqjHc9bzwfqbgZNe96\n9i1lDQ8Px93dnc2bNxMeHm4zrmiB1axZMxITExk/fjyLFi2iY8eOzJo1ixdeeMEc0759e5KTk5k6\ndSphYWHk5ubi7+9v3ripcExru1gXbZsxYwYjR44kICCA69evm2eJy7L7ddG28uZjTVhYGAaDgS1b\nttw0tjA+Pj6el156ibNnz+Lp6cm9997LunXrLB7fZO85DRw4kLS0NCZPnsy1a9cYMGAAo0aNsnh+\ncfFzGjZsGHXq1CE2NpYpU6ZQq1Yt7r33Xl599VWb+X/zzTdcvnz5lgp3qRzK/7RsKa/88+d/cHYO\nZkZjU94yPH3zQCcYn5/I5/XHOzuNEh7Neot9Tyx0dhpWdfjPq+Tm3frSKUer5+lFWvohZ6chDpKS\nsovQUMdvqCIidxajseltmVWsCFOnTuXUqVOsWLHC2alUCX5+fkRERDBp0iRnp1JC//79ycvLsyiI\nb1VkZCR5eXnExcU5bEy5NQaDAVs1ltHYFGzUvZoZFrmD5Obl8n3sJmenUYJvtO2/rIuIiFQ20dHR\ntGnThrS0tEpzT21ldfDgQdzd3ZkwYYKzUyE7O5v4+HgeeeQR7rrrLtasWcO6detITEx02DHOnj3L\nqlWr2L9/v8PGFOdRMSwiIiIiUoSXlxeZmZnOTqNKCAoK4siRI85OAzDNDm7YsIE33niD7OxsWrdu\nzYoVK3jqqaccdgwfHx/OnTvnsPHEuVQMi4iIiIhIlefu7s7GjRudnYZUIVV1N+kpwGogDcgDTt0k\nvg3wKXAe+AVIBnraiHUBxgFHgGzgNDAHqO2AsUVERERERKQSqKrF8F+BMOA4cAEobXeGAGA7cD/w\nJhAN1AX+H/CQlfh5wFzgAPAqpqL7T8BnlLzx2t6xRUREREREpBKoqsuk/YH0gp8PYHvWFuANwBO4\nD9hX0LYcOAjEAW2LxAYBkcAa4A9F2k8BfwcGAR+Uc2wRERERERGpJKrqzHB6GePqAH2BJP5XrAJc\nBd4FWgOdirQPLnifX2ycBOAa8PwtjC0iIiIiIiKVRFUthssqGKgBfG3lux0F7x2LtHUCcoHUYrG/\nAXuxLG7tHVtEREREREQqiTu9GG5S8G7tCcyFbU2LxWcBOTbi6/O/peX2ji0iIiIiIiKVxJ1eDBfe\nS/yble9+LRZT+LO1WGvx9o4tIiIiUu0ZjUYMBkOFvYxGo905Xrp0iUaNGpGWllbu81y6dCkeHh7l\n7m/Lyy+/zJNPPunwcW/Vzp07cXFx4fTp07f9WElJSbi4uHD+/Hlz29q1a2nVqhVubm4MHTqUrVu3\nloipzKydkyNMnz6d9u3bl7v/2bNnadCgwR373O07vRi+VvBe08p37sViCn+2FlsYn18k3t6xRURE\nRKq9CxcukHM0q8JeFy5csDvH2NhYwsPD8ff3ByA9PR0XF8tfm9999106dOiAh4cH9erV43e/+x2v\nvfaa+ftBgwZx6tTNnv5pv8Ii3x72FqnFz/d2FWpl4efnx9y5cy3aunTpQmZmpsUfOoYNG8Yf/vAH\nTp8+zYIFC3jwwQdLxNxKDlu3brWrT/Fr/umnn/LAAw/g7e2Nh4cH7dq1Y/jw4aWek7MUPV8fHx8G\nDx7MzJkznZzV7VFVd5MuqzMF79aWKxe2FV3mfAbTDtBulFwq3RTTEuob5Rzb7NVXJ5h/Dg5uT3Bw\n+f9ac6vCwsL4FR+nHb80YYTxfW1bf5twnrBrYRxq8ouz07AqLCyMXdcznJ1GCWFhYaSk7HJ2GuIg\n339/hpQUZ2chInJ7XL9+nYSEBFauXGkzZvHixYwZM4b58+fz0EMPkZOTw/79+0kp8j9Hd3d33N3d\nbY5RXvn5+eTnl/ZU0TuLtcLfzc2Nhg0bmj9fuHCB8+fP07t3bxo3bmxuLxpTHjk5Obi5uZXrDxBF\nbd68mWeffZYZM2awbNkyXF1dOXz4MGvXrjXHFD8nZyp+vi+++CI9e/Zk9uzZ1KlTx4mZla7wd819\n+/azb9/+MvVxVDFcE9vLi51pP6a8HrTyXWjB+84ibanAw5ieG/xVkXZ34PeYdo4u79hmb78911qz\nUyQlJdHX4Py/QFmTlJ/EpPohzk6jhKSsJP5ed6Cz07AqKSmJ9x+f6uw0SkhKSiIxcYWz0xAHSUmB\n0ND7nJ2GiMhtsWnTJrKzs+nVq5fNmHXr1jFgwACLmb02bdrwzDPPmD8vXbqUyMhIrly5ApiWq65Z\ns4aYmBhiYmI4d+4cDz30EO+++y533303ADdu3CA6Opply5ZhMBgYOnQoV69e5fDhw2zZssVmPrNn\nz+af//wnZ86cITAwkEmTJvHcc8/d6qUo1YYNGxg7diwZGRl06tSJUaNGlYjZvn07U6ZMYefOnXh7\ne9O3b1/efPNN8/LxsLAwgoKC8PLyIiEhARcXF1588UVmz56NwWAgLCyMjIwMoqOjiY6OxmAwkJub\nS1JSEr169SIrK4t9+/aZ/1sVviclJZGXl2eOKZxtLUs+99xzD7Vr12b58uW0bNmSHTt2lDgve332\n2WeEhoYyefJkc1tAQABPPPGE+XPRczIajeZ/P2vXruVPf/oT6enpdO7cmcWLF+Pn52fu98YbbzB/\n/nyys7N55pln8PPzY8mSJaWuSliyZAmxsbGcOnWK5s2bExERwZgxY2wW/B07dsTT05N169YxePBg\nqzGVQeHvJsV/R1m5cpXNPvYsk34MmF6s7RXgCqbHCX2AaUa1MvkF+AwIw7T7c6G6wB+BY8A3Rdo/\nxLQUemyxcYYDtYCiv83bO7aIiIiIVHLJycmEhISUKAyKfm7cuDE7duywexl0eno6q1evZu3atXzx\nxRfs3r2bmJgY8/dz5sxh2bJlvPfee6SkpJCTk8PKlStLnZWMiYlhyZIlxMfHc/jwYaZMmcLIkSP5\nz3/+YzX3siqtz3fffUe/fv3o06cPe/fuJTIykokTJ1r02b9/P3369KFfv37s27ePxMRE9uzZw9Ch\nQy3GWrFiBTVq1ODrr7/m7bffZv78+Xz44YcAfPLJJ/j6+jJt2jQyMzP58ccfS+TSpUsXDh48CEBi\nYiKZmZk88MADJeLKms/777+PwWDgq6++Yvny5WW/YMUU//dy+PBh9u3bV0qPkn777TdmzZrF0qVL\n+frrr7l48aLFHx1WrVrFjBkzeOONN/j2229p3bo18+bNK/W/XUJCAjExMbz++uscOXKEuXPn8uab\nbxIfH19qLp07d7Z7qXhVYM/McBRwrsjndpiex3sS03N/B2KaWZ3nqORK8QLQouDnBpiK8MLpsHTg\n/SKxU4CHgC8KcruCqbhtDDxebNwDQBzwKrAG+BzTeUZimhUuvl7GnrFFREREpJI7fvw4zZs3t2jz\n8/MjNzfX/HnatGns3buXgIAAAgMDuf/+++nduzeDBw/mrrts/3p948YNi421RowYwZIlS8zfL1iw\ngMmTJ9O/f38A5s+fz4YNG2yOd/XqVebNm8fGjRvp0qULAC1atGDHjh3ExcXx2GOPAVjkXhbFz7e4\nhQsX4ufnx4IFCwBo3bo1x44ds7hnOjY2loEDBzJu3DjANBMaHx9PSEgIWVlZ1K9fH4CgoCCmT58O\nQGBgIAkJCWzevJlBgwbh7e2Nq6srHh4eNpcQu7m50aBBA8C0OZutuLLm4+/vT2xsrEXf8tz7XfT6\nRUZG8uWXX/L73/8eX19f7r//fsLDw3n++edLXXZ848YN4uLiaNWqFQBRUVEWxfuCBQsYMmSIuW3y\n5Mls2bKF48eP2xxz5syZxMbG8vTTTwOmfy+TJk0iPj6eV155xeb5NmvWjAMHDthxBaoGe4rhdpiK\nw0IDMe2afD9wCVOh+CIVUwwPBXoU/Fx408SMgvckLIvhk0AXYBYwGdOzgXcBjwD/tTL2WEwF9QhM\nBe054O/A/1mJtXdsEREREanErly5go9P6fupNGrUiO3bt3Pw4EG2bt3K9u3bGTlyJPPmzWPbtm3U\nqlXLar8WLVpY7DDduHFjfvrpJ8C0g/XZs2fp3LmzRZ/OnTvz3XffWR3v0KFD/Prrr/Tp08diNjAn\nJ4eWLVuW6XzL4/Dhw4SGhlq0Ff+8a9cuTp48aZ7lBdP9zgaDgZMnT1K/fn0MBgPBwcEW/YpeE0cq\nSz4A993n+NuAateuzfr160lLS2PLli2kpKQwZcoU3njjDVJTU20W8DVr1jQXwmC6NtevX+fixYvU\nq1ePo0ePMnLkSIs+nTt35tixY1bHO3fuHN9//z0jRoywmGG+ceOG1fiiPD09uXTpUllOt0qxpxj2\nxnJmOBxTwVd4VbZScbOhPe2MPwL0K2NsHvBWwcvRY4tUS3e5umI0Vs7Hbtfz8iLt1CFnpyEiIpWE\nl5eX+T7fmwkKCiIoKIjRo0ezbds2unXrxkcffcRLL71kNd7NzfKOQoPBQF5eXqnHKG2zrMK+69ev\nLzGbXfxYjmQwGG66iVd+fj7Dhw83z8QW1aRJE/PP5bkm5VGWfAwGw23dIMrf3x9/f3+GDRtGTEwM\nrVu3ZuHChUybNs1qfPFVBoV/8Cjv9Snst2jRIh580Nq2R7ZdvnwZb2/vch23MrOnGP6Z/y1N9gA6\nATFFvncDXB2Ul4jcQW7k5nJ5g+0lO85kfLxdpSzU3e5yI+dG8U3tK4dHHnmE0ND3nJ2GiMhtERgY\nyPbt2+3u165dO8C0dLk8vLy8aNSoEampqYSFhQGmAu6bb76xKB6Luueee6hZsybp6enmPhWhXbt2\nrFmzxqItpdhjBkJCQjhw4ID58VTlVaNGDbuXeVvjqHwcpUWLFtSqVavc/14A2rZtS2pqKi+//LK5\nLTU11eY9wz4+PjRp0oQTJ07w/PPP23WsjIwMi1nqO4U9xfB2YBRwENNmWm5YLpsOAEre1S4iUond\nyL1BztEsZ6dRglub+vx2vnI+wqvP00/cPEhEpIrq1q0bcXFx5iW01kRERNC0aVN69uyJr68vP/74\nI6+//jp16tShd+/e5T72mDFjmD17Nq1bt6Zdu3YsWrSIzMxMmja1/kdbDw8PoqKiiIqKIj8/n27d\nuvHLL7+QkpKCq6urxW7XjjRq1Cjmzp3L2LFjiYiIYP/+/SxatMgiZtKkSYSGhhIREcGIESPw8PDg\nyJEjrF+/nnfeeQco22Oi/Pz8SE5O5rnnnqNGjRrm5cz2clQ+5TF9+nSys7N57LHHaN68ORcvXuTv\nf/87165do2/fvuUed8yYMQwZMoROnTrRtWtXPvnkE1JTU0t9VvFf/vIXIiMjqVevHo8++ig5OTl8\n++23nDlzxmK36+J27tzJnDlzyp1rZWVPMTwd07Lojwo+L8dUGINpV+qnAdt7vouIiIhIteft7Y1b\nm/IVNOU9nj3Cw8Nxd3dn8+bNhIeHW43p3bs3ixcv5p133jE/Cqdjx45s3LiRwMBAc1zRYtrWs2qL\ntkVFRZGZmcmQIUMwGAwMGTKE/v37c/bsWZvjzJw5Ex8fH+bMmUNERASenp506NCBiRMn2jxHPz8/\nevbsabF5180UPWazZs1ITExk/PjxLFq0iI4dOzJr1ixeeOEFc0z79u1JTk5m6tSphIWFkZubi7+/\nv3njJlvXpHjbjBkzGDlyJAEBAVy/ft08S1zabt/W2sqbjzVhYWEYDIZSH3dVPD4+Pp6XXnqJs2fP\n4unpyb333su6devo2rWrzXO42TkNHDiQtLQ0Jk+ezLVr1xgwYACjRo2yeH5x8XMaNmwYderUITY2\nlilTplCrVi3uvfdeXn31VZv5f/PNN1y+fPmWCvfKyt591u/GtGHUJUz3CBfyBl7CVAzvdUxqd6z8\n8+d/cHYOZkZjU94yPH3zQCcYn5/I5/XHOzuNEh7Neot9Tyx0dhpWBa+P4PvYTc5OowTf6PBKu0za\n85FWmhm2U5+nn9Bzo0XkpozGprdllq0iTJ06lVOnTrFihfP/X9ehQwe6d+9u3rn5Vl27do369euz\nZMkSBg4c6JAxqxs/Pz8iIiKYNGmSs1MpoX///uTl5VkUxLcqMjKSvLw84uLiHDamoxkMBmzVWAW3\nw1mte+2ZGQbTfcPrrLRfwPSYJRERERGRKi06Opo2bdqQlpZWofeYnj59mg0bNtCjRw9ycnJISEjg\nwIEDvPee4/Zp2LJlC6GhoSqEy+ngwYO4u7szYcIEZ6dCdnY28fHxPPLII9x1112sWbOGdevWkZiY\n6LBjnD17llWrVrF//36HjVmZ2FsMg+mRRr2BhsBcTLsp1wVCgP2YCmMRERERkSrJy8uLzMzMCj+u\ni4sL//rXv5g4cSJ5eXkEBQXx+eefExIS4rBjPP744zz+eEU9AObOExQUxJEjR5ydBmCaDd2wYQNv\nvPEG2dnZtG7dmhUrVvDUU0857Bg+Pj6cO3fu5oFVlD3FsCvwAfBMwef8gs9HgFzgU0zF8V8dmaCI\niIiISHXg6+vLl19+6ew0pIpwd3dn48aNzk6jSnOxI3YSpk2yxgPtsFx3nQ18AjzquNRERERERERE\nbg97iuEXgX9hujf4ZyvfHwECrbSLiIiIiIiIVCr2FMN+mJ41bMtFTLtKi4iIiIiIiFRq9hTDVwDb\nT3CGAODOvbtaRERERERE7hj2FMNfAc/b6OMNDMX0nGERERERERGRSs2eYvivQGvgv8ATBW2/B0YB\nuzE9XmmWQ7MTERERERERuQ3sKYZ3YtpNui2wuKBtDhAPuAP9gIMOzU5ERERE7ihGoxGDwVBhL6Ox\ntLv8rLt06RKNGjUiLS2t3Oe5c+dOXFxcOH36dLnHKKukpCRcXFw4f/68uW3t2rW0atUKNzc3hg4d\nytatW0vEVGbWzskRpk+fTvv27R06piNkZWXh4uJCcnLybT9Weno6Li4ufPvtt+a2bdu2ERwcTM2a\nNenVqxcZGRklYhzht99+o1mzZuzZs8eh45aXPcUwwL8xbaT1FDAZmAIMAPyBLxyamYiIiIjccS5c\nuE61Z7gAACAASURBVMBv53+psNeFCxfszjE2Npbw8HD8/f2B/xUPhW5XoVYWfn5+zJ0716KtS5cu\nZGZmWhT+w4YN4w9/+AOnT59mwYIFPPjggyVibiWHrVu32tWn+B8GPv30Ux544AG8vb3x8PCgXbt2\nDB8+vNRzcpZbPV9rxWdFCQsLIzIy0qKtefPmZGZm8rvf/c7cNmbMGDp06EBaWhqJiYk0a9asRMyt\n5LBs2TIAatasybhx44iJibnlcR2hrMWwB6b7gYcBvwKfAbOBNzE9X/jabclORERERKQCXb9+/f+z\nd+fxMd3748dfM5LIvklij4gQYimxlMYSaVpcO7X8KCKorUorofalt3a+cote9MZWWkrcWoqSipTQ\nkKot9khoGeoilliT/P6Y5DSTTJKZmCz0/Xw85pHM53zO57zPEeQ9n42VK1cycODA4g5FL5VKlaPM\n3NwcNzc35f3du3e5c+cO7777LuXLl8fOzi5HnYJ4/vy5EoO+OAwVGRlJz5496dy5M7/88gu//fYb\n8+fP16ljinhN5WXvt6RRq9W4ublRqlQppezy5cu0bt2aihUr4ujoqLeOsZ49ewbkfH59+vRh7969\nXLlypeA3YSKGJsMPgEaFGYgQQgghhBDFbd++fTx+/JiAgACjztu9ezc1a9bEysqKli1bcuHChRx1\nYmJiaNWqFTY2NlSqVIkRI0bw4MED5bi/vz8jR45k4sSJuLq6UrZsWUJDQ0lPT1eOJyUlERoailqt\nVhKVrD3VUVFRlClTBoCAgABl6K2+3mxD4hkxYgQhISG4ubnRvHlzo55JbrZv307Tpk359NNPqVGj\nBtWqVaNDhw6sXLlSqZM93tWrV2NnZ8dPP/1EnTp1sLW1JSAggMTERJ22Z8+eTdmyZbG3tyc4OJiZ\nM2dStWrVPONZtWoVPj4+WFlZ4e3tzeLFi5VnXliOHj1Kw4YNsbKywtfXl19++SVHnfj4eNq3b4+9\nvT1ly5alT58+3Lx5UzkeFBREx44dCQsLo1KlSjg7OxMcHMzjx4+V49HR0SxduhS1Wq30Vmftqc78\nPjk5meDgYNRqNWvXrtXbm21oPHPnzqVSpUq4u7sDkJ6ervM8y5UrR+PGjfn2229N/lyNZcww6RNA\nrcIKRAghhBBCiOIWHR2Nr69vjp7AvHoGr127RpcuXWjTpg0nTpxg1KhRjBs3TuecU6dO0aZNG7p0\n6cLJkyeJiIjgt99+Izg4WKet9evXY2FhweHDh1myZAmLFy9m48aNAGzdupVKlSoxbdo0NBoNN27c\nyBGLn58fZ85ol/GJiIhAo9HQrFmzHPUMjefrr79GpVJx8OBB1q5dm8/Ty13WZ1G+fHnOnj3LyZMn\njWrj6dOnzJkzh9WrV3P48GHu3bvHsGHDlOPffvstM2fOZPbs2fz666/UqFGD//u//8vzz27lypVM\nmjSJf/7zn5w7d46FCxcyd+5cli1bZvxNZpHXNR8+fEj79u3x8vIiLi6OOXPmEBISolPnxo0btGzZ\nknr16nH06FEiIyN5+PAhnTt31kksf/75Z+Lj44mMjGTjxo1s3bqVsLAwAP71r3/RrFkzgoOD0Wg0\naDQaKlWqpHMdd3d3bty4gbW1NWFhYWg0Gnr27JkjZkPjOXDgAKdPn+bHH38kMjJSeRbZn0eTJk2M\nHnpeGMyMqDsN7ZDoH9CuKC2EEEIIIcRr5eLFi0qPViYPDw9SU1NzPefLL7/Ew8NDSUJq1KjBhQsX\nmDJlilJn/vz59OrVi48//hiAatWqsWzZMnx9fbl9+zYuLi4A1K5dm+nTpwPg5eXFypUriYyMpHfv\n3jg5OVGqVCns7OxyHUJsbm6Oq6sroF2sLLd6hsbj6emZYwhzQYa3Zn1+o0aN4ueff6Z+/fpUqlSJ\nN998k8DAQN5//31sbGxybePFixcsXbqU6tWrAxASEqKTvIeFhTFw4ECl7NNPP2X//v1cvHgx1zY/\n++wz5s+fT7du3QCoUqUK48ePZ9myZYwcOdIk95vdhg0beP78OatWrcLa2hofHx8mT55Mv379lDpf\nfvkl9evXZ/bs2UrZmjVrKFOmDHFxcTRqpB206+DgwL///W9UKhXe3t706NGDyMhIPv30U+zt7bGw\nsMDa2jrXnwO1Wk3ZsmVRqVQ4ODjkWs/QeKysrAgPD8fc3Fypt39/zt13K1euzPfff5/rMyoqxiTD\n7wNJwF60vcQX0D9XOFhPmRBCCCGEECXegwcPKFu2rFHnnD17lqZNm+qUZX8fFxfH5cuXlV5e0A4f\nValUXL58GRcXF1QqFfXq1dM5r3z58ty6dcvIu8ifIfEANGzY0OTXtra2ZseOHSQkJLB//36OHDnC\nhAkTmD17NrGxsbkmZKVLl1YSYdA+m2fPnnHv3j0cHR05f/48Q4cO1TmnSZMmeoesA/z555/8/vvv\nfPDBBzo9zC9evDDBXebu7NmzvPHGG1hbWytl+n5eoqOjsbOz0ynP/PPJTD59fHxy9LrrG3L9sgyN\np06dOjqJcG7s7e1JTk42eZzGMiYZHpDl+/oZL30kGRZCCCGEEK8kBwcHnXmzhlCpVPnOMU1PT2fI\nkCFKT2xWFSpUUL7PnkioVCrS0tKMiscQhsSjUqny7Kl9WZ6ennh6ejJo0CAmTZpEjRo1+PLLL5k2\nbZre+mZmuqlLZhJY0OeTed7y5ct56623CtRGQRny89KhQwcWLFiQ41jWDwv0PZPC+nkxJJ6sCX5e\n7t+/j6Ojo8niKyhjkmFjt2ESQgghhBDileLl5UVMTIxR59SqVYstW7bolB05ckTnva+vL6dPn1a2\nayooCwuLPIfgGspU8ZhKlSpVsLKy4tGjRwVuo2bNmsTGxhIUFKSUxcbG5jp/t2zZslSoUIFLly7x\n/vvvF/i6xvLx8WHNmjWkpKQoyaO+n5dNmzbh7u6eI+HNKr9Vri0sLEzS021oPIZKSkqiRo0aL93O\ny5IEVwghhBBCiAwtWrTg+PHjRq0mPGzYMBITExkzZgznz59n8+bNLF++XKfO+PHjiY2NZfjw4Rw/\nfpxLly6xY8cOneG52Vfd1cfDw4Po6GiuX7/O7du3jbu5QoinIKZPn8748eM5cOAAV65c4fjx4wQH\nB5OSkkKnTp0K3O7o0aNZvXo1q1at4uLFi8ybNy/PZBhgxowZzJs3j8WLF3P+/HlOnz7N2rVrmTNn\nToHjyE+fPn0wMzMjODiY+Ph49u7dy+eff65TZ+TIkSQnJ9OrVy9iY2NJSEhg3759DB06lIcPHyr1\nDPl5iY2NJSkpidu3bxf4z9PQeAwVGxtLy5YtCxSLKRUkGVYDDYH3Ml6+wOuz8ZYQQgghhCg0Tk5O\nlHa2LbKXk5OTUfEFBgZiaWmprISbm6wJVuXKlYmIiGD37t3Ur1+fsLAw5syZo1Onbt26REdHk5iY\niL+/P/Xr12fixImUK1dOp019q1hnLZs5cybXrl2jWrVqOnObDVn92hTx6OPv70/r1q3zrZe1/pUr\nVxgwYAA+Pj60bduWq1evsm3bNp3tm4y9p169ejFlyhQ+/fRTfH19iY+PZ9iwYZQuXTrXexo0aBDh\n4eGsW7eO+vXr07JlS7766qs8e8yNvd/scdrY2LBjxw4uXryIr68v48aNY968eTnm/h46dAi1Wk3b\ntm2pU6cOH374IZaWlsr9GPLzEhISgoWFBT4+PpQtW5Zr167liMeQmAsajz43b94kLi6O3r1751u3\nsBmbxLYDlgFVspUnAiOA3SaI6XWXfufOH8Udg8LZuSKLVN2KOwy9PkmPYJfLJ8UdRg7tbi/iZIcv\nizsMvertGM7v8/cVdxg5VAoN5P7u3FdyLE72bavz/HzBP1kvLObeLjy9Y/wnrUWhTbcORESsL+4w\nhBAlnLNzxULfq7WwTJ48mStXrrB+vfxbZwgPDw+GDx/O+PHjizuUHLp27UpaWppJVy4uyff7Kli4\ncCE//fQTO3fuNFmbKpWK3HIsZ+eKkEvea8yAbz/ge+ARsBiIzyj3AQZmHAsADhnRphBCCCGEECVK\naGgo3t7eJCQklJg5tSXVmTNnsLS0ZOzYscUdCo8fP2bZsmW0bdsWMzMztmzZwrZt24iIiDDZNUrS\n/b6Knj59yuLFi9m+fXtxhwIYlwxPBW4CTYDsO3zPB2Iz6rQxTWhCCCGEEEIUPQcHBzQaTXGH8Uqo\nXbs2586dK+4wAG3v4O7du5k9ezaPHz+mRo0arF+/ns6dO5vsGiXpfl9FpUuXVoZqlwTGJMNvAgvJ\nmQiTUbYCCDFFUEIIIYQQQghhDEtLS/bu3VvcYYhXiDELaFkA9/M4/iCjjhBCCCGEEEIIUaIZkwyf\nA3qjvzfZDOgJnDVFUEIIIYQQQgghRGEyJhlehnao9E9AB6BqxqtjRlnTjDpCCCGEEEIIIUSJZsyc\n4a+A6kAo0DzbsXRgXkYdIYQQQgghhBCiRDMmGQYYD4QDndH2CgNcBrYBF0wYlxBCCCGEEEIIUWiM\nTYYBzqPtBRZCCCGEEEIIIV5JxswZ9kQ7P1iVy/FOgMfLBiSEEEIIIV5fzs7OqFSqIns5OzsbFV9y\ncjLlypUjISGhkJ5A0Vq9ejV2dnav1DW6d+9OWFiYydoTIjfG9Az/E6gMbM/l+CfANaDfywYlhBBC\nCCFeT3fv3iU5RVNk13OwLmdU/fnz5xMYGIinpyeJiYl4enqSlpYGQFRUFAEBAUpdFxcXGjVqxJw5\nc6hXr55J4y4IDw8PRo0axdixY4v0ur1796ZDhw4G11er1SQmJuLu7q73+KRJk2jXrh1DhgzB2tra\nVGEKkYMxPcPNgR/zOP4j0OLlwhFCCCGEEKJ4PHv2jJUrVzJw4MA868XHx6PRaNi5cyd3796lbdu2\n3L9/v8DXNBWVKrcBnIXL0tISFxcXk7Xn6+uLq6srmzZtMlmbQuhjTDLsBtzI4/ifgHEfvQkhhBBC\nCFFC7Nu3j8ePH+v0/urj5uaGm5sbjRs3ZtGiRWg0GmJjY0lISKBz586UL18eW1tbGjZsyM6dO3XO\n9fDwYMaMGQQHB+Pk5ES/ftpBlTExMbRq1QobGxsqVarEiBEjePDggXKev78/I0eOZOLEibi6ulK2\nbFlCQ0NJT09XjiclJREaGoparaZUqVI61/3pp5+oU6cOtra2BAQEkJiYqHN8+/btNGzYECsrKzw9\nPZk8eTLPnz9XjkdERFCvXj2sra0pU6YM/v7+3Lp1C8g5TPratWt07tyZMmXKYGNjQ61atdi4caOB\nfwpanTt35ptvvjHqHCGMZUwynAx45XG8GvAgj+NCCCGEEEKUWNHR0fj6+ur0sObX21q6dGkAnj59\nysOHD2nfvj379u3j5MmTdO/enW7dunH+/HmdcxYtWoSPjw9xcXHMmjWLU6dO0aZNG7p06cLJkyeJ\niIjgt99+Izg4WOe89evXY2FhweHDh1myZAmLFy9WksytW7dSqVIlpk2bhkaj4caNv/qwnj59ypw5\nc1i9ejWHDx/m3r17DBs2TDm+Z88e3n//fT766CPi4+MJDw9n8+bNTJw4EQCNRkPv3r0ZOHAg586d\nIzo6mv79++f6TEaMGMGTJ0+IiooiPj6exYsX4+joaPAzBWjcuDGHDh1ShqgLURiMmTMcDQwGwsjZ\nQ1wu49jPJopLCCGEEEKIInXx4kWdeaweHh6kpqbmqJfZG/u///2PGTNmYG9vT5MmTXB1ddWZOzxx\n4kS2b9/O5s2bmTRpklLu7+9PSEiI8r5///706tWLjz/+GIBq1aqxbNkyfH19uX37tjIEuXbt2kyf\nPh0ALy8vVq5cSWRkJL1798bJyYlSpUphZ2eHm5ubTrwvXrxg6dKlVK9eHYCQkBCdRPvzzz9n3Lhx\nDBgwAICqVasyZ84c+vXrx/z587l+/TovXryge/fuyvOpXbt2rs/x6tWrdO/enbp16wJQpUoVneP6\nnml27u7upKSk8Mcff1C5cuV86wtREMYkw7PQrhh9HFiY8RWgATAWsMuoI4QQQgghxCvnwYMHlC1b\nNt96Hh4eADx69IgaNWrw3Xff4erqyqNHj5gxYwY7d+7kxo0bPH/+nCdPnvDGG28o56pUKho1aqTT\nXlxcHJcvX9YZSpyeno5KpeLy5cu4uLigUqlyLNJVvnx5ZahyXkqXLq0kwpnnPXv2jHv37uHo6Ehc\nXBxHjx5lzpw5Sp20tDSePHnCzZs3qV+/PoGBgdSpU4d3332XwMBA3nvvvVznCY8ePZphw4axe/du\n3n77bbp27Yqvr2++cWZlb28PaFf3lmRYFBZjkuHjQHdgFTA327HbwHvAURPFJYQQQgghRJFycHDQ\nmaebm6ioKJydnXF1dcXW1lYpDwkJYc+ePSxcuJDq1atjZWVF//79cyySZWNjo/M+PT2dIUOGKD3D\nWVWoUEH53tzcXOeYSqUyaBixmZnur/yZw5Qzz01PT2f69On06NEjx7kuLi6o1Wp+/PFHjhw5wo8/\n/sh//vMfJkyYwIEDB/Suoh0cHEybNm344Ycf2LdvH2+99RYTJkxg2rRp+caaKXNBsqzDq4UwNWOS\nYYAdQBWgDZD58dIFYA/w2IRxCSGEEEIIUaS8vLyIiYnJt17VqlX17l986NAhBgwYQNeuXQF48uQJ\nly5dwtvbO8/2fH19OX36NJ6engULPIOFhYVBQ5D1Xf/s2bP5Xr9p06Y0bdqUqVOnUrt2bTZt2pTr\nllIVK1ZkyJAhDBkyhHnz5hEWFmZUMpyUlIS1tbXOhwFCmJqxyTBACrDV1IEIIYQQQghRnFq0aMHS\npUuVIcrGqlGjBhEREXTq1AkzMzNmzJjB06dPlTnGuRk/fjxNmzZl+PDhfPDBB9jZ2XHu3Dl27NjB\nv//9b0Dbe5tfOx4eHkRHR9O3b18sLCwM3u5o6tSpdOjQgSpVqtCjRw/MzMw4ffo0R48eZe7cuRw5\ncoR9+/bRtm1b3NzcOH78ONeuXcPHx0dve6NHj+Yf//gH1atX5/79++zatSvPOcb6xMbG4ufnh1pt\nzHq/QhinIMlwJifgH0AFIB7YmXd1IYQQQgjxd+fk5ISDddHtxunk5GRw3cDAQCwtLYmMjCQwMFBv\nnbyS5EWLFjFo0CBatGiBs7MzY8aM4enTp/km1nXr1iU6OprJkyfj7+9Pamoqnp6edOvWTee62dvJ\nXjZz5kyGDh1KtWrVePbsmdJLrO/6Wcveffdddu7cyWeffcaCBQswMzPD29uboKAgQDtUOSYmhiVL\nlnDv3j3c3d2ZOnUqffr00dteeno6o0aN4tq1a9jZ2REYGMjChQtzvX8PDw9at27NqlWrlLLt27fr\nLDImRGHI7yOvrkAQ8AFwM0u5L9oh01n/JfsJaAc8R+Ql/c6dP4o7BoWzc0UWqbrlX7EYfJIewS6X\nT4o7jBza3V7EyQ5fFncYetXbMZzf5+8r7jByqBQayP3dF4s7DL3s21bn+fnbxR1GDubeLjy987C4\nw9CrTbcORESsL+4whBAlnLNzxXx7MkuiyZMnc+XKFdavl3/nikJKSgouLi6sWrWKXr16AdoFxdq1\na0diYiLW1tbFHKF4FahUKnLLsZydK0IueW9+4w56Au7oJsKgXUSrHLABGA3sAwKAkQZH/PpQAx8D\n59DOm74KLADkb64QQgghxCsmNDSUyMhIEhISijuUv4X9+/fTtGlTJREGmDVrFpMnT5ZEWBS6/IZJ\nN0TbA5yVL1AX2A68n1G2FIgFegCLTRngK+D/gFFABDAf8AE+QrvlVCDw6n0kKoQQQgjxN+Xg4IBG\noynuMP422rdvT/v27XXKtmzZUkzRiL+b/JLhskD2sY0tM76uy1KWBmwBxpkorldFbbSJ8Ba0HwRk\nugL8C+gNfFMMcQkhhBBCCCGEyEN+w6T1ja1unPH1YLZyDWDD38v/y/iavTd8JdpVt99HCCGEEEII\nIUSJk18yfBXtcN+smgPX0Ca/WTkAd0wU16uiMZCKdoh4Vk+BE/z1wYEQQgghhBBCiBIkv2R4N9re\nzY5oF4QaA1QGtump2wBt8vx3UgG4jf4VtP8AXHi57auEEEIIIYQQQhSC/JLhBcAD4PuMr4uA+xnl\nWVmiTZijTR1gCWeNthdYnydZ6gghhBBCCCGEKEHy67XUAE2AEKA6cAlYCCRlq9cUiAG+M3WAJVwK\n2t5ffSzRriSdUnThCCGEEEIIIYQwhN7Nh4XB9qDdX9manEOlDwFeaFfkziq9SpUqyhtHR0ecnJzw\n82uGn1+zHBc4dOgwhw4dzlFuqvoLF4SRmpaao1y8mtQqFWnpJW83L7VKTVp6WnGHIUzEw8ODxMTE\n4g5DL7VaTVpayftZU6tVpKWVvL+booBUlMyNE0tYXFFRUaSXwP+ThBCvH5VKxaRJE/Dza8bJk6c4\nefKUcmzDhm8hl7xXkuGX8xkwCe12U1lX17YE/gdEAe2znZN+584fRRKcEEIUhm7d+vL9D98Wdxh6\nOViX48r97JsdFL+q9s3Ze3VecYeh1zvu4wg70qu4w8hhdNONDP3Wt7jD0Gt57195e1aV/CsWsciJ\nSXgPLl/cYSjOf3VDbzLs7OzE3bv3iiwOJydH7ty5a3D95ORkvL29iYmJwdPTsxAjMy21Ws3mzZvp\n1q1bkV1z27ZtzJgxg7i4uCK7phD6qFQqcsuxnJ0rQi55ryzu9HI2AhPRLiyW9bevIYAVsL44ghJC\nCCGEKKnu3r1XpB9aVbVvblT9+fPnExgYiKenJ4mJiXh6eiojTqKioggICMhxzpgxY1i0aJFJ4s1P\nUFAQ//vf/9i+fbtOuUajwdHR0eTXqlq1KtOmTdN7vFOnTkyZMoXvvvuOHj16mPTaQhQFSYZfzmlg\nKfAhsAXYBdQCRqHtFd5QbJEJIYQQQgijPHv2jJUrV7JhQ96/wsXHx+Ps7Ky8t7Yu/vVS3dzcTN6m\nSqVCpcp7IGm/fv1YunSpJMPilZTfatIif2PQLjBWG1gC9AT+BXQozqCEEEIIIYRx9u3bx+PHj/X2\n/mbl5uam87K1tSUxMRG1Ws2vv/6qU1etVhMREQGg1ImIiOCdd97BxsaG2rVrs2/fPp1zzp07R6dO\nnXB0dMTOzo633nqL06dPM336dNauXcvOnTtRq9Wo1Wqio6NzXAfg1KlTBAYGYm1tTZkyZRg4cCD3\n799XjgcFBdGxY0fCwsKoVKkSzs7OBAcH8/jxY51Y8pv33alTJ6Kjo7lx40ae9YQoiSQZfnlpaLec\nqol2rnBltMmxrCIthBBCCPEKiY6OxtfXV6c3VF/P6MsuDDZp0iTGjBnDyZMnady4Mb179+bRo0cA\nXL9+nebNm1OqVCn27dvHiRMnGD16NKmpqYSGhtKzZ0/eeecdNBoNGo2GZs1yLpD66NEj2rRpg729\nPUePHmXr1q3ExMQQHBysU+/nn38mPj6eyMhINm7cyNatWwkLC9Opk1/PcPXq1XF0dOTAgQMv9UyE\nKA4yTFoIIYQQQgjg4sWLuLu7K+89PDxITc2564aHh4fO+7Nnzxp1nU8++YT27bVrrM6aNYu1a9dy\n4sQJ3nrrLZYuXYqdnR3fffcdZmbaX9WzLuRlaWmJhYVFnsOiN2zYQEpKCuvWrcPGxgaAFStW0Lp1\naxISEpT2HBwc+Pe//41KpcLb25sePXoQGRnJp59+CsCqVavyvReVSkXlypW5ePGiUc9AiJLAmJ7h\nNKBPHsd7A7JHjxBCCCGEeCU9ePAAW1vbfOtFRUVx4sQJ5VW+vHGreNerV0/5PvPcW7duAXD8+HGa\nN2+uJMIFcfbsWd544w0lEQZo1qwZarWa+Ph4pczHx0en57d8+fJKHMawt7cnOTm5wPEKUVxM2TOs\nQrZqEkIIIYQQrygHBwcePHiQb72qVavqLKAF2jm7oDuE+vnz53rPNzc3V77PTEYzV6xWqVT5DsPO\nb+hy9jhyOzd7wq1SqQq0V/v9+/dNvpK1EEXBlHOGKwP5/+shhBBCCCFECeTl5cXVq1cLdK6rqyug\nnfOb6bfffjO6nQYNGnDw4MFcE2kLCwtevHiRZxs+Pj6cOnWKhw8fKmUxMTGkpaVRq1YtpcyQpDo/\n6enpXLt2jerVq790W0IUtfyS4c5AeMYL4IMs77O+vgemAUcKJ0whhBBCCCEKV4sWLTh+/HiBFsiy\nsrKiadOmzJ07l/j4eGJiYggJCTG6nREjRvDw4UN69uzJsWPHuHTpEt988w0nTpwAtL3Sp0+f5sKF\nC9y+fVtvYty3b1+sra3p378/p0+fJjo6mqFDh9K9e3ed+ccvuxAYwIULF7h37x4tWrR46baEKGr5\nDZNuAARled8y45XdQ+AQMNI0YQkhhBBCiNeRk5MjVe2bF+n1DBUYGIilpSWRkZEEBgbqrZNXb2p4\neDiDBw+mcePGeHl5sXTpUlq21P3VOb/e2AoVKhAdHU1oaCitW7dGpVJRr149VqxYAcCQIUOIioqi\nUaNGPHz4kKioqBzXsLKyYs+ePYwZM4YmTZpgaWlJly5ddFaK1reHcH77Ck+fPp2ZM2fqDKXetm0b\nLVu2pEKFCnnelxAlUX7J8PSMF2gX0OoHrC/EeIQQQgghxGvszp27xR1CriwsLPjggw9YtWqV3mTY\n399f7+rSmWrWrMnBgwd1yrImjrmtTp19nq6Pjw87d+7Uew0XFxf27NmTbxt16tTJsX9xVvpWip42\nbRrTpk3L9ZwrV67Qpk0b5X16ejrr1q1jypQpuZ4jRElmzAJanoDxy8sJIYR4rVhaWuJgXa64wxBC\niEIRGhqKt7e3zhZEQpv47t+/n59++kkp2759O+bm5vTo0aMYIxOi4IxJhhP1lJmjnVfsBGwHNCaI\nSQghRAn20Ucj2LDhP8Udhl7OzhWLOwQhxCvOwcEBjUZ+pc1OpVLlWFysU6dOdOrUqZgiEuLlGbOa\n9DzgaJb3KmAfsAlYDpwGqpkuNCGEEEIIIYQQonAYkwy3BbJOgugItECbJPfJKJtgoriEEEII307k\nBgAAIABJREFUIYQQQohCY8ww6crAhSzvO6IdOv1pxvvaQF/ThCWEEEIIIYQQQhQeY5JhCyDrRmat\n0Q6TznQFkDXVhRBCFBtHR4ci3bJFCCGEEK8uY5Lh34G3gJVoe4E9gaxrr7uh3W9YCCGEKBYJCfHF\nHYJesrCXEEIIUfIYkwx/A0wFXIE6wAPghyzH6wOXTReaEEII8XpwcLTnHfdxxR2GEEIIIbIwJhme\ng3becFfgHtAPyNw13RHtFkv/Z9LohBBCiNfAlYSzxR1CrqTXWgghxN+VMatJPwEGAc5oh0hvy3Ls\nPlAe3WHTQgghhBBC6HBydkSlUhXZy8nZ0egYk5OTKVeuHAkJCYXwBEq+Dz/8kNatWxf5devUqcOM\nGTOK5FpVq1Zl0aJFynuNRsO7776Lra0tpUqVAsDDw0Onjigct2/fRq1WEx0drff4tm3baNiwYaFc\n25ie4bykoe0tFkIIIYQQIlf37iaz9+q8IrteQaYozJ8/n8DAQDw9PQFITEzE09OTtLQ0AKKioggI\nCOD27ds4OzsD8Oeff9K2bVtUKhW7du3C1dXVdDdRDFQqlfJ9UFAQVatWZdo0w/u9/P39GThwIAMG\nDAC0ieWoUaMYO3ZsntfMel1TmD59Olu2bOHUqVM65ceOHcPa2lp5v2DBAjQaDSdOnMDOzg6AuLg4\nnTovE0NSUhKrVq0y+Jzsz9zf35+6devyxRdfKHVWrlzJhx9+yJIlSxgyZMhLx1lSZP/71qlTJ6ZM\nmcJ3331Hjx49THotY3qGAdyBVcAfwHMgIKPcNaO8selCE0IIIYQQomg9e/aMlStXMnDgQIPPSUpK\nonnz5jg6OhIVFfXKJMIvXrzI9Vh6erryfUGS1OznFEai+zLKlCmDlZWV8v7SpUv4+vpSrVo13Nzc\n9NYxVlpaGmlpaQW67/ye3+zZs/noo4/45ptvXplE+NmzZwU+t1+/fixdutSE0WgZkwxXBY4B3YAz\nQKksx/4EGgGDTReaEEIIIYQQRWvfvn08fvyYgICA/CsD8fHx+Pn5UadOHXbt2oWtrS2gHWr9wQcf\nULZsWezt7fH39ycuLg6AR48eYW9vz5YtW3Ta2rt3LxYWFty6dYvevXszfPhw5djkyZNRq9X88ssv\nSlnlypXZsGEDoE1eP/vsMypXroylpSX16tVj27a/ZjUmJiaiVqv59ttvCQgIwNramhUrVpCWlkZI\nSAjOzs44Ozvz8ccfk5qamuM+sybHhsrrnFu3btG5c2esra3x8PAgPDw8R528niHA6tWrsbOz46ef\nfqJOnTrY2toSEBBAYmKicnzmzJmcOXMGtVqNWq1m7dq1gO4QaA8PD7Zt28batWtRq9UEBwcr5QsX\nLjQ6nl27dlGnTh1Kly7N2bNnC/Ts8np+Y8eOZfbs2ezcuZNu3bop5du3b6dhw4ZYWVnh6enJ5MmT\nef78OQAzZ86kbt26Odry8/NjzJgxnD9/HrVaza1btwBISUmhdOnStGvXTqn71VdfUb16deX9qVOn\nCAwMxNramjJlyjBw4EDu37+vHA8KCqJjx47MnTuXSpUq4e7uDsDRo0eVOH19fXV+pnPTqVMnoqOj\nuXHjRr51jWFMMvw52uHQdYE+eo7/AMjmjkIIIYQQ4pUVHR2Nr69vjt48fb17hw8fpkWLFrRr147N\nmzdjYWEBaJOY9u3bc+PGDXbu3Mlvv/1Gy5YtCQgIQKPRYGNjQ58+fXIkgOHh4XTs2BE3Nzdat25N\nVFSUciyzxzmz7NKlS/zxxx/4+/sDsHjxYhYsWMD8+fM5ffo0Xbt2pVu3bpw4cULnGhMmTODDDz/k\n7NmzdO7cmQULFvDVV1+xYsUKjhw5QmpqKhs2bDDo/vOT1zlBQUEkJCQQGRnJf//7X9atW6cksYY8\nw0xPnz5lzpw5rF69msOHD3Pv3j2GDRsGQO/evRk7dize3t5oNBo0Gg09e/bMEduxY8cIDAykV69e\naDQawsLClDqZ9QyN58mTJ/zzn/9k5cqVnD17lipVqhS4Vzz7Oc+fPycoKIivv/6a/fv363xgs2fP\nHt5//30++ugj4uPjCQ8PZ/PmzUycOBGAQYMGce7cOY4ePaqcc/78eQ4fPsygQYPw9vamXLly7N+/\nH4CYmBgcHByIiYnRmR6QOZf80aNHtGnTBnt7e44ePcrWrVuJiYlRPkjIdODAAU6fPs2PP/5IZGQk\nDx8+pH379nh5eREXF8ecOXMICQnJ996rV6+Oo6MjBw4cMPo55sWYOcOBwBLgKuCi53gS2tWmhRBC\nCCGEeCVdvHhR6cHK5OHhobe3tHv37nTp0oWVK1fqlO/fv58TJ07w559/YmlpCWh75rZv3866desI\nDQ1l8ODBNGvWjOvXr1OhQgXu3r3L999/z+bNmwFo1aoVw4cP5+bNm9jZ2XHs2DE+++wzIiMjGT9+\nPFFRUXh5eVGhQgVAO+c1NDSU3r17AzBjxgyio6NZsGAB69atU2L76KOPdHoTFy9ezPjx43nvvfcA\nCAsLY8+ePTr3Y8xc16zPIDcXLlxg9+7dHDp0iGbNmgGwZs0aZY62oc8QtEO9ly5dqvRYhoSEKAmZ\npaUlNjY2mJmZKUOf9XFxccHCwgIrK6tc6xkaT2pqKkuWLKFBgwbKucbMtc6U/Zmnp6ezatUqUlNT\nOX78OHXq1NE5/vnnnzNu3DhljnbVqlWZM2cO/fr1Y/78+VSsWJG2bdsSHh5O48bama3h4eE0atRI\n6TFu1aoV+/fvp1evXkRFRfHee++xa9cuYmNjadq0KQcOHGDu3LkAbNiwgZSUFNatW4eNjQ0AK1as\noHXr1iQkJCh/llZWVoSHh2Nubq7Uef78OatWrcLa2hofHx8mT55Mv379lHvR9/dNpVJRuXJlLl68\naPSzzIsxPcP2wPU8jltgugW5hBBCCCGEKHIPHjxQhjrnp2vXrmzfvp3IyEid8ri4OFJSUnB1dcXO\nzk55nTlzRlmhOjMJWbNmDaBNLsqUKaMMS61Zs6bSUxcTE4OXlxc9e/bk0KFDvHjxgqioKKVX+P79\n+9y4cQM/Pz+dOJo3b058fLxOWaNGjZTvk5OT0Wg0SkIK2qTjzTffLPDQXkOcPXsWtVpNkyZNlDJ3\nd3clsYfcn+Hp06d1VvkuXbq0ztDd8uXL8+zZM+7dM25t3/x6bg2Nx8zMjPr16xt1bUPj8/Pzw8nJ\niYkTJ+aYfxsXF8c///lPndj69u1LSkoKN2/eBGDIkCF8++23PH36lNTUVNatW8egQYOUNvz9/ZWR\nB5m9wJll2UcinD17ljfeeENJhAGaNWuGWq3W+ZmrU6eOkghnPS/rwmRNmzY16BnY29uTnJxs2AMz\nkDHJ6+9A7TyOvwlcerlwhBBCCCGEKD4ODg48ePDAoLpLlizBxcWFjh078v333/POO+8A2oWTypYt\ny8GDB3OcY29vr3w/ePBgwsLCmDBhAuHh4QwYMEAnKcvsqcscNl2lShVcXFw4evQo0dHRzJkzJ8/4\n0tPTcyR5WZOXvM4rCnkloIY+QzMz3XQms83Mob2mYmg8pUuXLrSFwmrXrs2//vUv3n77bbp27crW\nrVt1huZPnz5d72rLLi7aQb3/+Mc/sLa2ZvPmzUpi2afPX7NfM0cjXL58mbi4OFq3bk1KSgobNmzA\n1dWVatWq6XxgkdvPSdb717cad0F/vu7fv4+jo/FbpeXFmGR4CzAcCCdnD3F3oCeyz7AQQgjxSrF3\nsGN0043FHYYQJYaXlxcxMTEG1VWpVHzxxReYm5vTqVMntm7dStu2bWnYsCE3b95EpVJRtWrVXM/v\n06cPoaGhLFmyhOPHj7Np0yad4/7+/ixcuJCyZcsyZswYpWzFihX8/vvvSi+dvb09FSpU4ODBgzr7\nAx88eJDatXPvy3JwcKB8+fIcPnxYaSs9PZ3Y2FgqVqxo0DMoiJo1a5KWlsYvv/yi9EpfvXqV69f/\nSjF8fX0Neob5sbCw0DvE3Vimiudl1a5dW9naq3Pnzvz3v/+ldOnS+Pr6cvbsWZ2h5tmZmZkRFBRE\neHg4Dg4OdO/eXdlGCv4ajfD555/j5eWFi4sLrVq1YuTIkTg5Oen8bPn4+LBq1SoePnyojKTInF9c\nq1atXGPw8fFhzZo1pKSkKInykSNH8r3v9PR0rl27pjMKwBTyS4avAKOBbcAsoANwBMjcEXl8RnkT\n4DdgoZ42hBBCCFFCJV45V9wh6OXsXHi/iAuRlxYtWrB06VK9vaq5WbRoEWZmZnTt2pXNmzfTvn17\n/Pz86Ny5M/PmzVMWcNq9ezfvvPMOzZtr15x1dHSkR48ehISE0KpVK6pVq6bTrr+/P8OHD+fq1atK\nsurv78/gwYN15gsDhIaGMnXqVKpXr46vry9ff/01Bw8e1NmXVp/Ro0cze/ZsatSoQZ06dVi2bBka\njaZQk2Fvb2/atm3L0KFDWbFiBZaWlnzyySc62xi98847Bj3D/FStWpWkpCSOHz9O5cqVsbe3V3pT\ns0pPT8+zx9JU8RRU1vhq1qzJgQMHaN26NR07dmTbtm1MnTqVDh06UKVKFXr06IGZmRmnT5/m6NGj\nyjxf0I5GmDNnDqVKlWLv3r05rtOqVSu+/vprZREyDw8PXFxciIiIYPXq1Uq9vn37Mm3aNPr378/M\nmTO5c+cOQ4cOpXv37nkm5H369GHSpEkEBwczdepU/vjjDz7//PN87//ChQvcu3ePFi1aGPrIDJJf\nMlwFyJw0kQy8BcwE+maUvQPcA5YCk4DHJo1OCCGEEEK8VhydHHjHfVyRXs8YgYGBWFpaEhkZSWBg\nYK71sifK8+bNw9zcnO7du/Pdd9/xww8/MHnyZIYMGcKtW7coW7YszZs3JygoSOe84OBg1q5dqzN3\nM1PmCr8uLi6UKVMG0CbDqampSnKc6aOPPuLBgweMGzeOmzdvUrNmTSIiInS209GX3I8dOxaNRsPg\nwdodUvv370/fvn05dy73D8qmT5/OzJkzX2oo8urVqxkyZAgBAQG4uroybdo0/vzzT506hjxDffeU\ntax79+5ERETw9ttvc+/ePVavXk3//v31npPfhx8FjUffvQcHB5OYmJhjsbbcZI+vevXqHDhwgICA\nADp06MD27dvZuXMnn332GQsWLMDMzAxvb+8cP29Vq1alVatWXLt2jVatWuW4jr+/P5s2bdL5+Wrd\nujVr167VKbOysmLPnj2MGTOGJk2aYGlpSZcuXZSVuPXFDNph+jt27GD48OH4+vpSq1Yt5s2bR+fO\nnfO8/23bttGyZUudD4BMIb8/rTTgfWCDnvNcM77+mVFPGCb9zp0/ijsGIYQosCNH4mjatGFxhyFe\nc87OFRn6rW9xh6HX8t6/8vasKsUdRg6RE5PwHly+uMNQnP/qRpHNPTW1yZMnc+XKFdavX1/o19q4\ncSPDhg3jxo0byirFJd2AAQO4desWu3btKu5QXknTpk0jIiKCEydOoFYbs56xafj4+NCvXz8mTJhQ\n5NcuiPT0dN544w2mTJmid040aBPv3HKsjJFGevPegq7+nA7cKuC5QgghhBBClFihoaF4e3vrbBFj\nao8fP+bGjRvMmjWLDz744JVJhNPT09m/fz8//fRTcYfyytq1axdLly4t8kT4zz//ZPPmzVy9epWh\nQ4cW6bVfxvbt2zE3N881EX4ZhiTDLoBh/fdaVwsYixBCCCGEEMXOwcEBjUZTqNeYO3cus2bNokWL\nFkyZMqVQr2VKKpWKq1fl1/2XERsbWyzXLVu2LK6urixfvhxnZ+diiaEgOnXqRKdOnQqlbUOS4cUZ\nL0OkA6UKHo4QQgghhBCvv+nTpzN9+vTiDkP8jZh6u6nXgSHJ8M9oV5U2xKs5MUQIIYQQJYq9gy3L\ne/9a3GEIIYR4jRmSDC8n5wJaQgghhBCFJvHK+eIOIVey7ZMQQrwein75MiGEEEIIIYQQophJMiyE\nEEIIIYQQ4m/nZZJhG2Aq4GGaUIQQQgghhBBCiKKRXzIcAOzL5ZgdMB0onM3XhBBCCCGEEEKIQpJf\nMhwF3CqCOIQQQgghxN+Ao5MDKpWqyF6OTg5Gx5icnEy5cuVISEgo8H0eO3YMtVpdJHvyRkVFoVar\nuXPnjlL2/fffU716dczNzQkODubAgQM56ojCsWDBAqpWrZrr8e7duxMWFlaEEYncGLKatBBCCCGE\nECaRfO8+YUd6Fdn1RjfdaPQ58+fPJzAwEE9P7QDIxMREPD09lX1ao6KiCAgI4Pbt2zg7O5s03vx4\neHgwatQoxo4dq5T5+fmh0Wh0Yhk0aBAffPABo0aNwtbWFktLyxx1XiaGNWvW0KpVK4PPUavVJCYm\n4u7urjzPY8eO4evrC0BKSgrdu3fn/Pnz7N27l2rVqr10nCXF9OnTSUpKYtWqVQBMmjSJdu3aMWTI\nEKytrYs5ur83WUBLCCGEEEKIDM+ePWPlypUMHDiwuEPRS6VS5SgzNzfHzc1NeX/37l3u3LnDu+++\nS/ny5bGzs8tRpyCeP3+uxKAvjoK6e/cugYGBXL9+nZiYmFcmEc58HvnJ/qx8fX1xdXVl06ZNhRGW\nMMLLJMP/Qzun+FcTxSKEEEIIIUSx2rdvH48fPyYgIMCo83bv3k3NmjWxsrKiZcuWXLhwIUedmJgY\nWrVqhY2NDZUqVWLEiBE8ePBAOe7v78/IkSOZOHEirq6ulC1bltDQUNLT05XjSUlJhIaGolarKVWq\nFKA7TDoqKooyZcoAEBAQgFqtJjo6Wu9QakPiGTFiBCEhIbi5udG8eXOjnokhrl+/TosWLShVqhTR\n0dGUK1cO0H4oMX78eCpXroyNjQ1NmjThxx9/BCA9PR0vLy8WLlyo09bFixdRq9UcP36cTz/9lHbt\n2inHvvrqK9RqNRs3/jVSoHnz5nz++efK++XLl+Pl5UXp0qWpXr06X331lU77arWaZcuW0a1bN2xt\nbZk0aRIA8+bNo1y5ctjZ2TFgwAAePnyoc17mn19WnTt35ptvvinIIxMm9DLJ8HO0c4rvmSYUIYQQ\nQgghild0dDS+vr45evPy6gm9du0aXbp0oU2bNpw4cYJRo0Yxbtw4nXNOnTpFmzZt6NKlCydPniQi\nIoLffvuN4OBgnbbWr1+PhYUFhw8fZsmSJSxevFhJ4LZu3UqlSpWYNm0aGo2GGzdu5IjFz8+PM2fO\nABAREYFGo6FZs2Y56hkaz9dff41KpeLgwYOsXbs2n6eXO33P78KFC/j5+eHh4cHevXtxcPhrfvfA\ngQP5+eef+eabbzhz5gwDBgygY8eOnDx5EpVKxeDBg5Vhx5nCw8Np0KABDRo0oHXr1hw6dEhnaLuL\niwtRUVGAdlj2sWPHaN26NaB9tqNGjeKTTz7hzJkzjB49mhEjRrBjxw6da8yYMYMOHTpw+vRpRowY\nwaZNm5gyZQqfffYZx48fx9vbm0WLFuncr76e9MaNG+vEJ4qHIXOGy2d8zfzbZgmMBLJ/xHEN+M5E\ncQkhhBBCCFHkLl68iLu7u06Zh4cHqampuZ7z5Zdf4uHhoSyKVKNGDS5cuMCUKVOUOvPnz6dXr158\n/PHHAFSrVo1ly5bh6+vL7du3cXFxAaB27dpMnz4dAC8vL1auXElkZCS9e/fGycmJUqVKYWdnl+uQ\nZ3Nzc1xdXQFwdnbOtZ6h8Xh6ejJ//nydc69cuZLrs8iNvucXFBRE48aN+f7775VeboDLly/z7bff\nkpiYSOXKlQEYOXIke/fuZfny5SxdupSgoCCmTp3KL7/8wptvvklqaipr165Vemv9/Px48uQJR48e\n5c033yQ6OpqQkBDCw8MBba+4mZkZTZo0AbSLXvXv358RI0YA8OGHHxIXF8fcuXPp0KGDElvv3r11\nPjDo06cPQUFBDBkyBICJEyeyf/9+Ll++rNSZNm1ajnt3d3cnJSWFP/74Q7lHUfTy6xn2Rpvk9s9S\nZgvMBxZke60HqhdCjEIIIYQQQhSJBw8eYGtra9Q5Z8+epWnTpjpl2d/HxcXx9ddfY2dnp7yaN2+O\nSqVSEieVSkW9evV0zitfvjy3bpl+cxdD4gFo2LChya+dqUuXLhw5ciTHcOFff/2V9PR0fHx8dOL7\n4YcflBW+y5UrR4cOHZTkdvfu3dy9e5e+ffsCYGtrS8OGDdm/fz+XLl0iOTmZkSNHcvXqVTQaDVFR\nUbz11luYmWn7Bs+dO4efn59OHH5+fsTHx+uUNWrUSOf9uXPncvS8N23aVO/Q6Kzs7e0B7crlovjk\n1zM8ELgDLNZzLIS/5gurgC3AIOBTk0UnhBBCCCFEEXJwcNCZN2sIlUqVb/KTnp7OkCFDlJ7YrCpU\nqKB8b25unqPtwhhKa0g8KpUKGxsbk18707hx42jYsCFBQUGkpqYyYMAAANLS0lCpVBw7dizH87Cy\nslK+Hzx4MH369GHx4sWEh4fTrVs3naHW/v7+7N+/H1dXV1q2bImNjQ1vvvkm+/fv58CBAzpzinOT\nfXizqZ7H/fv3AXB0dDRJe6Jg8kuGA4BtwFM9x35DO2c408aM+kIIIYQQQrySvLy8iImJMeqcWrVq\nsWXLFp2yI0eO6Lz39fXl9OnTynZNBWVhYZHnkG1DmSqelxUaGoq5uTmDBg0iNTWV4OBgGjRoQHp6\nOjdu3MDf3z/Xc9u0aYO9vT1ffvklO3bsYNeuXTrH/f39+eKLL3ByclLa8ff3Z8eOHRw9epS5c+cq\ndWvVqsXBgwd1VhE/ePAgtWvXzjP+WrVqcfjwYYKCgpSyI0eO5LvadlJSEtbW1jofhIiil98w6erA\ncQPbOkfRDZMeinZY9jkgFcjv47IKwFrgTyAFOAq8l0f9/mjvOwXQACsBFxO1LYQQQgghSqgWLVpw\n/PjxfHt6sxo2bBiJiYmMGTOG8+fPs3nzZpYvX65TZ/z48cTGxjJ8+HCOHz/OpUuX2LFjB8OGDVPq\npKen53tdDw8PoqOjuX79Ordv3zbu5gohHlMYM2YMYWFhfPDBB6xYsYIaNWrQt29fgoKC2LJlCwkJ\nCRw7dowFCxawdetW5bxSpUoRHBzMhAkTqFSpUo4VwJs3b87Tp0+JiIhQFsry9/dn06ZNmJubK/OF\nQZuUr1u3jmXLlnHx4kW++OILNmzYwLhx4/KMffTo0axZs4avvvqKixcvMnv2bGJjY/O959jYWPz8\n/FCrZafb4pRfz7AN8DBb2R2gHpCQrfx+Rv2i8CngjDZhtQYq5lHXGTiINpldBPwO9AU2AcHA6mz1\nPwYWou31/gioDHwCNAOaoE14C9q2EEIIIcTfmoOjPaObbsy/ogmvZ4zAwEAsLS2JjIwkMDAw13pZ\ne/4qV65MREQEn3zyCcuXL6dRo0bMmTOHfv36KXXq1q1LdHQ0kydPxt/fn9TUVDw9PenWrZtOm/pW\nsc5aNnPmTIYOHUq1atV49uyZ0ktsyOrXWcsKGo8+/v7+qFQq9u/fn2/d3OIbOXIkZmZmjBgxgrS0\nNFatWsXnn3/OuHHj+P3333F2dubNN9/k7bff1jkvODiYmTNn6t0X2sbGhkaNGnH+/HkaNGgAwJtv\nvomZmRnNmjVT5guDdqujL774ggULFjBmzBg8PDz48ssvad++fZ730bNnTxISEpg0aRIpKSl07tyZ\nTz75hDVr1uR53vbt2wkJCcmzjih8+f1030Kb5M0xoK0JaJNG15cNygDuwNWM73cA7YBSudSdh3Z+\nc0dgZ0aZGjgMVAOqAI8yyl2AJOAU2uQ386OwDmiHi08CZhew7Uzpd+78YdhdCiFECXTkSBxNmxbe\ngipClHTOzhV5e1aV4g4jh8iJSXgPLp9/xSJy/qsbRdKrWBgmT57MlStXWL9+fXGH8krw8PBg+PDh\njB8/vsiv/csvv9C8eXOuXLlCpUqVivz6BREXF0e7du1ITEzE2tq6uMN5LahUKnLLsZydK0IueW9+\n/fKngXcNjOEdtElkUbiafxVFH+ASfyWroB1W/QXant1/ZCnvAlhlHMv6r/cOtD3h779E20IIIYQQ\n4hUQGhpKZGSksnKxyN2ZM2ewtLRk7NixRXrdZ8+e8fvvvzNlyhS6dev2yiTCALNmzWLy5MmSCJcA\n+SXDmwF/oHM+9bpk1Nv88iGZVHm0c3qP6Dn2S8bXrOujN874ejiX+jXRDssuSNtCCCGEEOIV4ODg\ngEajKfbFpV4FtWvX5ty5czpDjovChg0b8PDw4M6dOyxatKhIr/2ytmzZwkcffVTcYQjyT4bD0S5S\ntRH4DO2w36w8gH9mHD+bUb8kyVyeTV+feWZZ1vnGFdD2COdWX5WlTWPbFkIIIYQQQphAUFAQL168\n4NixY1SsKL9yi4LJ7yOcJ2jny+5EO192ItqFsu4D9kDmRl7nMuo9MeLaDmgXqzJUGHDXiPrwVy+u\nvq2hnmSrY2x9Y9sWQgghxGvAzt6WyIlJxR2GEEKIl2TIeIYEwBcYjHbLoDpoe0XvAz8D3wFfYVwi\nDOAETEXbE5vfQl7paLcvMjYZzlz5ubSeY5bZ6mSvnz3JzV7f2LaFEEII8RpISjxf3CHolbFIjBBC\nCAMZOrj/MdpFob4w4bUTyX+Y9su6nvFV3/8OmWVZhzlfR5uYVyTn1lEV0S6OdT1LXWPaVnz44V8L\nDNSrV5d69erqqyaEECXS779f54i+1RKEEMXK39+fMva2xR2G4jw7ijsEIcTfyJEjcQCcPHmKkycN\nW9e5aGe6F70baBPSZnqONc34eixLWSwwBHiLnMlwU+A8f/X2Gtu2YsmShfnFLYQQJdaRI8jWSkKU\nQFFRUXh7lZytlYQQoihl/m6S/XeUDRu+zfWcwu6ZLQm+Qbvnb4csZaWAUWiHXf+Qpfx7tL3gH6L7\nbDoCVYHsm80Z07YQQgghhBBCiBLiVe0Z7gi8kfG9F9qhzZMyvt4FlmapOwfoAWwAFqEd3vz/gIZo\n50E/ylL3NjAFWADsA75FO+R5LNrVshdni8OYtoUQQgghhBBClBCvas9wN2Bmxqs62gURDv6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zNu3Diuu+469uzZQ/PmzenVqxcjR44sct+oUaOYNWsW1157bbFndO7cmRYtWtC0aVOaNGkChDB8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\nOoZhSZIkS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/8+7v6SRoVPpWOEfsR/ri0J67BPpI8FI/lj48q/TziaqSq9R/giZVT0s4Ow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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x548db10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Documentation in sqlparse for the mapping can be found here: \n",
      "# https://github.com/andialbrecht/sqlparse/blob/master/sqlparse/keywords.py\n",
      "# or here\n",
      "# https://github.com/andialbrecht/sqlparse/blob/master/sqlparse/lexer.py\n",
      "\n",
      "# Here we look at example of the SQL sequence that G-Test has indicated are good\n",
      "# indicators of SQL injections.\n",
      "dataframe[dataframe['sequences'].map(lambda x: \"('Single', 'Identifier')\" in x)].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>raw_sql</th>\n",
        "      <th>type</th>\n",
        "      <th>parsed_sql</th>\n",
        "      <th>sequences</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>    '; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Single, Identifier, Float, Float, Float, Erro...</td>\n",
        "      <td> [('Single',), ('Identifier',), ('Float',), ('F...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>                               anything' or 'x'='x</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Identifier, Single, Identifier, Single, Ident...</td>\n",
        "      <td> [('Identifier',), ('Single',), ('Identifier',)...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> '; exec master..xp_cmdshell 'ping aaa.bbb.ccc....</td>\n",
        "      <td> malicious</td>\n",
        "      <td>               [Single, Identifier, Error, Single]</td>\n",
        "      <td> [('Single',), ('Identifier',), ('Error',), ('S...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> '; if not(select system_user) &lt;&gt; 'sa' waitfor ...</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Single, Identifier, Single, Integer, Placehol...</td>\n",
        "      <td> [('Single',), ('Identifier',), ('Single',), ('...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td> '; if is_srvrolemember('sysadmin') &gt; 0 waitfor...</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Single, Identifier, Single, Integer, Placehol...</td>\n",
        "      <td> [('Single',), ('Identifier',), ('Single',), ('...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                                              raw_sql       type  \\\n",
        "0      '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious   \n",
        "44                                anything' or 'x'='x  malicious   \n",
        "49  '; exec master..xp_cmdshell 'ping aaa.bbb.ccc....  malicious   \n",
        "54  '; if not(select system_user) <> 'sa' waitfor ...  malicious   \n",
        "55  '; if is_srvrolemember('sysadmin') > 0 waitfor...  malicious   \n",
        "\n",
        "                                           parsed_sql  \\\n",
        "0   [Single, Identifier, Float, Float, Float, Erro...   \n",
        "44  [Identifier, Single, Identifier, Single, Ident...   \n",
        "49                [Single, Identifier, Error, Single]   \n",
        "54  [Single, Identifier, Single, Integer, Placehol...   \n",
        "55  [Single, Identifier, Single, Integer, Placehol...   \n",
        "\n",
        "                                            sequences  \n",
        "0   [('Single',), ('Identifier',), ('Float',), ('F...  \n",
        "44  [('Identifier',), ('Single',), ('Identifier',)...  \n",
        "49  [('Single',), ('Identifier',), ('Error',), ('S...  \n",
        "54  [('Single',), ('Identifier',), ('Single',), ('...  \n",
        "55  [('Single',), ('Identifier',), ('Single',), ('...  \n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataframe[dataframe['sequences'].map(lambda x: \"('Punctuation',)\" in x)].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>raw_sql</th>\n",
        "      <th>type</th>\n",
        "      <th>parsed_sql</th>\n",
        "      <th>sequences</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> create user name identified by pass123 tempora...</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [DDL, Keyword, Identifier, Keyword, Identifier...</td>\n",
        "      <td> [('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>    grant connect to name; grant resource to name;</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Keyword, Keyword, Keyword, Identifier, Punctu...</td>\n",
        "      <td> [('Keyword',), ('Keyword',), ('Keyword',), ('I...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> insert into users(login, password, level) valu...</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [DML, Keyword, Function, Keyword, Punctuation,...</td>\n",
        "      <td> [('DML',), ('Keyword',), ('Function',), ('Keyw...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>                                \\'; desc users; --</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Error, Error, Punctuation, Order, Identifier,...</td>\n",
        "      <td> [('Error',), ('Error',), ('Punctuation',), ('O...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>    1' and 1=(select count(*) from tablenames); --</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Integer, Error, Keyword, Comparison, Punctuat...</td>\n",
        "      <td> [('Integer',), ('Error',), ('Keyword',), ('Com...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "                                              raw_sql       type  \\\n",
        "2   create user name identified by pass123 tempora...  malicious   \n",
        "6      grant connect to name; grant resource to name;  malicious   \n",
        "7   insert into users(login, password, level) valu...  malicious   \n",
        "12                                 \\'; desc users; --  malicious   \n",
        "21     1' and 1=(select count(*) from tablenames); --  malicious   \n",
        "\n",
        "                                           parsed_sql  \\\n",
        "2   [DDL, Keyword, Identifier, Keyword, Identifier...   \n",
        "6   [Keyword, Keyword, Keyword, Identifier, Punctu...   \n",
        "7   [DML, Keyword, Function, Keyword, Punctuation,...   \n",
        "12  [Error, Error, Punctuation, Order, Identifier,...   \n",
        "21  [Integer, Error, Keyword, Comparison, Punctuat...   \n",
        "\n",
        "                                            sequences  \n",
        "2   [('DDL',), ('Keyword',), ('Identifier',), ('Ke...  \n",
        "6   [('Keyword',), ('Keyword',), ('Keyword',), ('I...  \n",
        "7   [('DML',), ('Keyword',), ('Function',), ('Keyw...  \n",
        "12  [('Error',), ('Error',), ('Punctuation',), ('O...  \n",
        "21  [('Integer',), ('Error',), ('Keyword',), ('Com...  \n",
        "\n",
        "[5 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: left; padding:10px;\"><img src=\"files/images/hilarious.jpg\" width=\"400px\"></div>\n",
      "<font size=4>\n",
      "The results above are a mixed bag of both 'legit' and 'malicious'. As we'd expect ('Punctuation') can't really be used as a 'signature' to effectively differentiate malicious sql statements. However, it's cool that the data transformation into the parsed tokens helps us generalize and find interesing malicious patterns. \n",
      "<br><br>So even though these individual features can't be used to differentiate.. when we build a 'feature vector' of a set of features, machine learning algorithms can use those vectors to build non-linear functional decision boundaries in multi-dimensional spaces (and we laugh at this point because it's hard to take yourself serious after saying a bunch of fancy stuff...). BTW the image is a total non sequitur.\n",
      "</font>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Generating additional feature dimensions for the machine learning to expand its mind into...\n",
      "# We're basically building up features to include into our 'feature vector' for ML\n",
      "import math\n",
      "from collections import Counter\n",
      "def entropy(s):\n",
      "    p, lns = Counter(s), float(len(s))\n",
      "    return -sum( count/lns * math.log(count/lns, 2) for count in p.values())\n",
      "dataframe['length'] = dataframe['parsed_sql'].map(lambda x: len(x))\n",
      "dataframe['entropy'] = dataframe['raw_sql'].map(lambda x: entropy(x))\n",
      "dataframe.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>raw_sql</th>\n",
        "      <th>type</th>\n",
        "      <th>parsed_sql</th>\n",
        "      <th>sequences</th>\n",
        "      <th>length</th>\n",
        "      <th>entropy</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    '; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Single, Identifier, Float, Float, Float, Erro...</td>\n",
        "      <td> [('Single',), ('Identifier',), ('Float',), ('F...</td>\n",
        "      <td>  7</td>\n",
        "      <td> 4.368792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>          create user name identified by 'pass123'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>       [DDL, Keyword, Identifier, Keyword, Single]</td>\n",
        "      <td> [('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
        "      <td>  5</td>\n",
        "      <td> 4.037326</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> create user name identified by pass123 tempora...</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [DDL, Keyword, Identifier, Keyword, Identifier...</td>\n",
        "      <td> [('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
        "      <td> 11</td>\n",
        "      <td> 4.028603</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>              exec sp_addlogin 'name' , 'password'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>             [Keyword, Identifier, IdentifierList]</td>\n",
        "      <td> [('Keyword',), ('Identifier',), ('IdentifierLi...</td>\n",
        "      <td>  3</td>\n",
        "      <td> 4.030493</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>      exec sp_addsrvrolemember 'name' , 'sysadmin'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>             [Keyword, Identifier, IdentifierList]</td>\n",
        "      <td> [('Keyword',), ('Identifier',), ('IdentifierLi...</td>\n",
        "      <td>  3</td>\n",
        "      <td> 4.010013</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "                                             raw_sql       type  \\\n",
        "0     '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious   \n",
        "1           create user name identified by 'pass123'  malicious   \n",
        "2  create user name identified by pass123 tempora...  malicious   \n",
        "3               exec sp_addlogin 'name' , 'password'  malicious   \n",
        "4       exec sp_addsrvrolemember 'name' , 'sysadmin'  malicious   \n",
        "\n",
        "                                          parsed_sql  \\\n",
        "0  [Single, Identifier, Float, Float, Float, Erro...   \n",
        "1        [DDL, Keyword, Identifier, Keyword, Single]   \n",
        "2  [DDL, Keyword, Identifier, Keyword, Identifier...   \n",
        "3              [Keyword, Identifier, IdentifierList]   \n",
        "4              [Keyword, Identifier, IdentifierList]   \n",
        "\n",
        "                                           sequences  length   entropy  \n",
        "0  [('Single',), ('Identifier',), ('Float',), ('F...       7  4.368792  \n",
        "1  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...       5  4.037326  \n",
        "2  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...      11  4.028603  \n",
        "3  [('Keyword',), ('Identifier',), ('IdentifierLi...       3  4.030493  \n",
        "4  [('Keyword',), ('Identifier',), ('IdentifierLi...       3  4.010013  \n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# For each SQL statement aggregate the malicious and legit g-test scores as features\n",
      "import numpy as np\n",
      "def g_aggregate(sequence, name):\n",
      "    try:\n",
      "        g_scores = [df_stats.ix[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\n",
      "dataframe['malicious_g'] = dataframe['sequences'].map(lambda x: g_aggregate(x, 'malicious_g'))\n",
      "dataframe['legit_g'] = dataframe['sequences'].map(lambda x: g_aggregate(x, 'legit_g'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dataframe.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>raw_sql</th>\n",
        "      <th>type</th>\n",
        "      <th>parsed_sql</th>\n",
        "      <th>sequences</th>\n",
        "      <th>length</th>\n",
        "      <th>entropy</th>\n",
        "      <th>malicious_g</th>\n",
        "      <th>legit_g</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    '; exec master..xp_cmdshell 'ping 10.10.1.2'--</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [Single, Identifier, Float, Float, Float, Erro...</td>\n",
        "      <td> [('Single',), ('Identifier',), ('Float',), ('F...</td>\n",
        "      <td>  7</td>\n",
        "      <td> 4.368792</td>\n",
        "      <td> 449.733570</td>\n",
        "      <td>  -63.831145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>          create user name identified by 'pass123'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>       [DDL, Keyword, Identifier, Keyword, Single]</td>\n",
        "      <td> [('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
        "      <td>  5</td>\n",
        "      <td> 4.037326</td>\n",
        "      <td>-242.191260</td>\n",
        "      <td> 1210.713063</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> create user name identified by pass123 tempora...</td>\n",
        "      <td> malicious</td>\n",
        "      <td> [DDL, Keyword, Identifier, Keyword, Identifier...</td>\n",
        "      <td> [('DDL',), ('Keyword',), ('Identifier',), ('Ke...</td>\n",
        "      <td> 11</td>\n",
        "      <td> 4.028603</td>\n",
        "      <td>-392.742728</td>\n",
        "      <td> 1489.732587</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>              exec sp_addlogin 'name' , 'password'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>             [Keyword, Identifier, IdentifierList]</td>\n",
        "      <td> [('Keyword',), ('Identifier',), ('IdentifierLi...</td>\n",
        "      <td>  3</td>\n",
        "      <td> 4.030493</td>\n",
        "      <td>-331.875793</td>\n",
        "      <td> 1069.265013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>      exec sp_addsrvrolemember 'name' , 'sysadmin'</td>\n",
        "      <td> malicious</td>\n",
        "      <td>             [Keyword, Identifier, IdentifierList]</td>\n",
        "      <td> [('Keyword',), ('Identifier',), ('IdentifierLi...</td>\n",
        "      <td>  3</td>\n",
        "      <td> 4.010013</td>\n",
        "      <td>-331.875793</td>\n",
        "      <td> 1069.265013</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "                                             raw_sql       type  \\\n",
        "0     '; exec master..xp_cmdshell 'ping 10.10.1.2'--  malicious   \n",
        "1           create user name identified by 'pass123'  malicious   \n",
        "2  create user name identified by pass123 tempora...  malicious   \n",
        "3               exec sp_addlogin 'name' , 'password'  malicious   \n",
        "4       exec sp_addsrvrolemember 'name' , 'sysadmin'  malicious   \n",
        "\n",
        "                                          parsed_sql  \\\n",
        "0  [Single, Identifier, Float, Float, Float, Erro...   \n",
        "1        [DDL, Keyword, Identifier, Keyword, Single]   \n",
        "2  [DDL, Keyword, Identifier, Keyword, Identifier...   \n",
        "3              [Keyword, Identifier, IdentifierList]   \n",
        "4              [Keyword, Identifier, IdentifierList]   \n",
        "\n",
        "                                           sequences  length   entropy  \\\n",
        "0  [('Single',), ('Identifier',), ('Float',), ('F...       7  4.368792   \n",
        "1  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...       5  4.037326   \n",
        "2  [('DDL',), ('Keyword',), ('Identifier',), ('Ke...      11  4.028603   \n",
        "3  [('Keyword',), ('Identifier',), ('IdentifierLi...       3  4.030493   \n",
        "4  [('Keyword',), ('Identifier',), ('IdentifierLi...       3  4.010013   \n",
        "\n",
        "   malicious_g      legit_g  \n",
        "0   449.733570   -63.831145  \n",
        "1  -242.191260  1210.713063  \n",
        "2  -392.742728  1489.732587  \n",
        "3  -331.875793  1069.265013  \n",
        "4  -331.875793  1069.265013  \n",
        "\n",
        "[5 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Boxplots show you the distribution of the data (spread).\n",
      "# http://en.wikipedia.org/wiki/Box_plot\n",
      "\n",
      "# Plot the length and entropy of SQL statements\n",
      "# Fixme Brian: make these pretty\n",
      "dataframe.boxplot('length','type')\n",
      "plt.ylabel('SQL Statement Length')\n",
      "dataframe.boxplot('entropy','type')\n",
      "plt.ylabel('SQL Statement Entropy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.text.Text at 0x6711210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+rcn2jbHndDkuqevsTyxJ3eV5VlXRyQz8b4AXdCuQxBBx8erhRC08jHekeaLJ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\ntUaqijwT+G2At+d4PGlWSfc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yPB/Ym7gN/S1FBdXGgcT/m74MjBUciyRJkjQjBokZ7H/K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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x548d750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SJGmiyayWUjRHzNXzLGWRNNOVbYS5bO9HxcmrlKVmK2BPYDNiXvMbgDWBJwK3\nA49MKsrOmZirrwwOwrJlRUchSdOrbIls2d6PitMqMV8t47k+C1wHfBM4hrF68nWAv1L84kNSz3n4\n4WrRIUhSaVVdwU0lkqXG/F3AB4GvAD8BflG37z7gPGBv4Eu5RSeVwAteUHQEkjT9Rhnoz4LZJkbr\n/lfqliz/yVwFLAH2AzYB7iBKWi5K9n8UOJyYUrFT2wOfAHYGtgDWIKZi/CXweaJMJs1SFkmSelzZ\nSj/K9n5UnLxKWbZj/Ch52p1Ewp7Fk4na9B8BHwHeB/wcOAj4I92felHquoULi45AkiT1gyyJ+cPA\nei32PxXIur7hRcBLgaOBE4CTgfcCbwM2BOZlPJ/Uc0ZGqkWHIEmlZY25yiRLYv574LVN9q0NvBW4\ndMoRhZuS50dzOp8kSZLU07LUmO9JlLJ8DzgFuJBIxu8GFgDPBV4M/HYScawFPIFI8J8BfAaYA+xK\nTMFYzxpz9byFC+Hcc+PnRYtg993j5333hSOPLC4uSZouZavJLtv7UXHynMf8ncSsLGum2h8BDgVG\nMp6v5vDkvDV/AF4H3Nygr4m5+kqlAn7TKmmmKVsiW7b3o+LkvcDQFsDrgR2S4/8O/JCYTWWynkzM\n0LI+MUPLEcQUjHsCS1N9TczVV+bOrbJ4caXoMCRpWk1XIlutVqlUKl1/HRNz5aVVYp5lHvOaW4Gv\nTiWgBv7BWGL/Y2KWlt8Tc6K/JufXkqbVbrsVHYEkSeoHk0nMp8PVwGJg90Y7h4aGGBwcBGD27NnM\nnTv38U/Ltbuz3Xa7V7Zf/3oe1wvxuO22225PxzZUqVZ7Jx7fj9tFbVerVUZGRgAez1+byVrK8iLg\nPcC2wMap4weIJbG2znjOZq4CnpK8Tj1LWSRJ6nFlK/0o2/tRcfJaYOgdwCXElIlrEjdm3lT3uDF5\nZLF5k/aXADsCv8p4Pqnn1D41S5Ly5zVWZZKllOVjRHnJy4G7cnr9E4iVPy8ikvu1iWkX30RMk/jh\nnF5HkiRJ6mlZSlkeAj4IfCPH138DcBDwbGBTohRmKfAz4LPAnQ2OsZRFkqQeV7bSj7K9HxUnr1lZ\nrgU2yiOgOmcmD0mSVDIDk5mUuUfNmVN0BJoJstSYfwo4jJhzXFKHrH+UNBONjk7PA6rT8jr33FP0\nb1QzQZYR8x8BGwJ/Bc4FbgBWNuh3TA5xSZIkSTNKli+ZdgB+QfsR8yyj8JNhjbn6ysKFcOSRRUch\nSeVk7bdpygPbAAAYEklEQVT6Tasa8yyJ+UXALsBHgd8A9zbptyzDOSfDxFx9Ze5cWLy46CgkqZxM\nzNVv8rr5cxfgC8BXc4hJmjFuu60KVAqOQpLKqorXWJVFlsT8fuCObgUilcnChXDuufHz7bdDbUXn\nffe1rEWS8jRvXtERSPnJkph/H9gP+HqXYpFKY+5cWL48fl60qPJ4Yj53bmEhSVIpjYxUig5Byk3W\nmz9PBW4FvkIsBNRoVpabcoirFWvM1VcGB2HZsqKjkCRJvSCvGvO/1P386iZ9RoFZGc4pld5mm1Wx\n/lGSuqNarVKpfS0p9bksiXkn85M7lC2lHHBA0RFIkqR+0I+L5VrKIkmSpL7UqpSl24sBSZIkdc3w\ncNERSPnJmphvAMwHLgWuA16QtG8CfAJ4en6hSeWwcGG16BAkqbQWLKgWHYKUmyw15psSCflWwBJg\nG2CdZN/dwDxgDnBUngFK/c5VPyVJUieyJObHApsDzwduZPxiQ6PAj4E98gtNKofBwUrRIUhSiVWK\nDkDKTZbEfG/geOBKonQlbSkwlENMUt+rVuMBsGDBWHulMrYKqCRJUr0sifkmRF15M6uAtacWjlQO\n9Qn4ZZdVGR6uFBiNJJVZFUfNVRZZbv68nagrb2Yu3V/1U+o7t91WdASSVF7z5hUdgZSfLIn5BcAh\nwJMa7NsVOAg4L4+gpDJ54hMrRYcgSaU1MlIpOgQpN1kWGNoC+AMwi7jR8+3A6cBawH7ALcBziRla\nuskFhtTz0jXm8+fHz9aYS5I0s7VaYCjryp9PBb5K3AhaO3YU+ClwKPB/kwsxExNz9ZW5c6ssXlwp\nOgxJKqVqtUrFEQ/1kVaJeZabPyFqyF8DbAhsn5z0ero/Si71lfoR86uuGluZzhFzSZLUTJYR84OA\nS4AbmuwfBF4MnDbFmNpxxFx9ZWgIRkaKjkKSJPWCViPmWW7+HAFe0GL/84FvZzifNCMMDhYdgSSV\nV+0bSakMsiTm7axB1JtLqnPVVdWiQ5Ck0lqwoFp0CFJu8krM5wCvBG7N6XxSadzQrPhLkiSpTrvE\nfD6xoufKZPs7yXb9YyVx8+ebgO93J0ypf82eXSk6BEkqsUrRAUi5aTcry1WM3czZ7ObPUeBB4HfA\nGblGJ/WphQvh3HPj50WLxmZi2XdfOPLIwsKSJEk9LMusLFXgWODC7oTSMWdlUV9xHnNJ6p6BgSqj\no5Wiw5A6ltc85pU8gpEkScrLvHlFRyDlJ+vKnzXrA7NpXKN+0+TD6Ygj5uorCxdaviJJkkKrEfOs\nifmbgaOBHYja8trxtZ9HgVkZzrcdcCDwcmBrYG1gCXAmsBB4qMExJuaSJEnqS3ktMLQv8F0i8f5m\ncsLvAT8EHgOuBI7JGNvBwJHAdcAC4IPA34ha9t8SibrU16rVatEhSFJpeY1VmWSpMf8gcC3wXGA9\n4F3AKcBFwI7ApcDijK9/JvAp4IG6thOJRP2/gUOAr2c8pyRJktR3spSy3E8k0Z8BNgbuBPYCfpns\n/xzwH8Dzc4hrJ2KqxhOAw1L7LGWRJElSX8qrlGUWcFfy84rkecO6/X8nEuo8PCV5vj2n80mSpBIa\nHi46Aik/WRLzfwBbJj8/RIyY/3vd/u2Af+YQ0yzg48C/iBp2qa9Z/yhJ3bNgQbXoEKTcZKkx/y2w\nJ/CJZPs84sbNFUSCfzhwfg4xLSTKYT5K1JpLkiRJpZelxnwXYmaWY4kR882AXwDPSvb/BXgVU5vH\n/JPETZ/fBA5t0scac0mSBMDAAJgWqJ/ktfLnFcmj5g7gOURivhK4Blg1uRABGCaS8lNonpQDMDQ0\nxODgIACzZ89m7ty5VCoVYKxswG233XbbbbfdLv829FY8brud3q5Wq4yMjAA8nr82k2XE/MXEdIl3\nNNm/KbHw0K8znLNmmCiRGSHmNm/FEXP1lWq1+vh/qJKkfA0MVBkdrRQdhtSxvGZlqRI15s28FLg4\nw/lqPpE8TqN9Ui5JkvS4efOKjkDKT5YR81XAgTSfKeUtwKlkK495D/BVoi7940B6KPw24MJUmyPm\nkiRJ6kt51Zi38wLG5jnv1L8Tyfi/EUl9WpWJibkkSZJUOu1KWd4H3AAsTbYXJj+nH8uJFTp/kvH1\n30bMWz4riSX92CPj+aSeM3aDkiQpb15jVSbtRszvA25Mfh4kRsTTN3+OElMl/g74Up7BSZIkSTNF\nlhrzZcQI+nndCaVj1phLkiSpL+U1K8sgxSflkiRJjxseLjoCKT9ZRszTtgH2B55ELC50CrAij6Da\ncMRcfcV5zCWpe5zHXP1mKrOyHAK8F3gZ42vLXwacA6xb1/ZuYmaWBycbqCRJkjRTtRsxPwfYDHhR\n6pglwJbAccDlwL7EDCvDwDG5RzmeI+aSJAmAgQEwLVA/aTVi3i4xXwqcCXy4ru1FwCXA6UD9elsX\nARsCz51soB0yMZckSYCJufrPVG7+3JQYHa+3W/L8w1T7T4GnZQ1OKjvn2JWkbqoWHYCUm3aJ+WPA\nmqm25yXPv0213w2slUdQkiRJnZg3r30fqV+0S8xvBF5Ytz2LGDG/Drg31XdjYgEiSXUWL64UHYIk\nldbISKXoEKTctEvMzwLeABwBPAP4DHEz6NkN+j4PuCHX6KQSOPfcoiOQJEn9oF1i/lUi2f4y8Gfg\n/cDNwBdS/WYDrwIuzjtAqd8tX14tOgRJKi3v41GZtJvH/D7g34F3EDd2Xg+cDCxP9Xs6MAJ8P+f4\npL60cOHYSPlVV0FtfaF994UjjywsLEmS1MOmsvJnUZwuUX2lUgEHdCRJEkxtukRJkqSeNTxcdARS\nfkzMpS7bccdq0SFIUmktWFAtOgQpN+1qzCVN0etfX3QEktQfkq/4J3Fc9mMsi1UvssZckiRJmibW\nmEuSJEk9Ls/EfAfgjTmeTyoF59iVpO7xGqsyyTMxfx1wRo7nkyRJkmaMPGvMjwaOofvlMdaYS5Ik\nqS9ZYy5JkiT1OBNzqcusf5Sk7vEaqzIxMZckSZJ6QJ415v8NfBJrzCVJkqSGWtWYt1v58wGg0yx4\nzQx9JUmSJNVpl5hfmfF8JuZSSrVapVKpFB2GJJWS11iVSbvEvDIdQUiSJEkzXZ415tPFGnNJkiT1\npanUmNdbA3g+sBPwBOB24Drg0inGJ0mSJM14nc6gMg+4AVgEfA04DjgFuAS4Hnhzqv/aGWL4KHAm\nsBRYlbyOVBrOsStJ3eM1VmXSyYj5McDRwD+B7wF/BO4HNgB2Bl4DfAfYBjgWWA84H9ijwxg+Bdyd\nnHdDvIFUkiRJM1C7GvPdgF8DFwJvAe5s0GdT4HTgpcBeRHK+C52XyQwCy5Kf/wysC2zdor815pIk\nSepLrWrM2yXmZwLPBZ4JrGjRbx3gL8CWRDnKwUSynpWJuSRJkkqrVWLersb8hcBptE7KSfaflrzI\nW5hcUi6VkvWPktQ9XmNVJu0S800YKzNp50ZitPyHUwlIkiRJmonaJebLgS06PNcTgXumFo5UPq5I\nJ0nd4zVWZdIuMf8DcEAH/VYjpkz8fR5BSZIkSTNNu5lTTgTOAb4FvAt4tEGfNYHjgR2Bj+caXRND\nQ0MMDg4CMHv2bObOnfv4J+ZarZnbbvfK9uLFiznyyCN7Jh633Xbb7TJtn3UWfO1rlZ6Jx22309vV\napWRkRGAx/PXZtrNygJxU+eBRA356cR84/cRc44/N9m3JTGX+UEdnK8VZ2VR6VSr1cf/Q5Uk5Wvu\n3CqLF1eKDkPq2FSmSwSYBQwDHwTWarD/YeDzSZ9Vkwmwjom5JEnqWKUCyeCk1BemmpjXbAa8iihZ\n2YBY/fNq4AIaLzzUqbcSI+4ARwBrAF9MtpcRI/H1TMwlSZrBFi6Ec8+Nnxctgt13j5/33ReSykGp\nZ+WVmHfLxUDynxS1jLsWVxXYI9XfxFx9xVIWSeoeS1nUb1ol5u1u/mxlDWAX4EnANcTKn5PxkinE\nIEmSJJXCam32V4CvAJun2rcCrgQuAX4A/C/w7byDk8rA0XJJ6p6hoUrRIUi5aVfKMgK8ENgu1b4I\n+A/gUuAK4OXAM4GDk2O6yVIWSZIk9aVWpSztRsx3AX6Rans6kZRfkjx/IOl3HXEjp6Q6VacLkKSu\n8RqrMmmXmD8R+HuqrZI8n1zXtgL4HvCsfMKSJEmSZpZ2iflaRNJdb5fkeVGq/WZgdh5BSWVijbkk\ndY/XWJVJu8T8ZqJ2vN5uwB3ATan2dYHlOcUlSZIkzSjtEvNfAwcBOyXbrwW2BX7WoO+OwD/yC00q\nB+sfJal7vMaqTNol5p8mylkWE6PkPwL+BXwh1W8WsA/wm7wDlCRJkmaCTlb+/HdgPvA04HrgWOCy\nVJ+XAV8CjgJ+mWeADThdoiRJkvpSq+kSO0nMe42JuSRJkvrSVOYxlzRF1j9KUvd4jVWZmJhLkiRJ\nPcBSFkmSJGmaWMoiSZIk9TgTc6nLrH+UpO7xGqsyMTGXJEmSeoA15pIkSdI0scZckiRJ6nEm5lKX\nWf8oSd3jNVZlYmIuSZIk9QBrzCVJkqRpYo25JEmS1ONMzKUus/5RkrrHa6zKxMRckiRJ6gHWmEuS\nJEnTxBpzSZIkqceZmEtdZv2jJHWP11iViYm5JEmS1AOsMZckSZKmiTXmkiRJUo8zMZe6zPpHSeoe\nr7Eqk15IzFcDjgKuBVYANwGfB9YtMihJkiRpOvVCjfmXgSOAs4GfAc9Iti8B9gTSBeXWmEuSJKkv\ntaoxX316Q5ngmUQS/iPgDXXtNwBfAfYHziggLkmSJGlaFV3K8ubkeWGq/STgIeDA6Q1Hyp/1j5LU\nPV5jVSZFJ+bPA1YCV6TaHwGuSvZLfW3x4sVFhyBJpeU1VmVSdGL+JOAu4F8N9v0D2ITiy22kKVm+\nfHnRIUhSaf38515jVR5FJ+brEqPjjTxc10eSJGmCa68tOgIpP0Un5g8BazXZtzYxI8tD0xeOlL9l\ny5YVHYIkldbDDy8rOgQpN0VPl/j/gD2IUfF0OculwLbA5qn2xcCzux+aJEmSlLurgLlFB9HIJ4FV\nwG6p9rWBfwIXTHtEkiRJ0gy0IzEry1mp9iOIhP2AaY9IkiRJmqG+QiThPwLeDnwBeBS4qMigJEmS\npJlmNeD9wLXETCw3A5/H2VhUrArxgXHeNLzWSPJakjTTjTDxejictD11EuebyrHStOuFOcJXAV9M\nHlKvGZ2m10i/zr7ETc4LpuH1JamXpK+Hja6RWc41HddxSVIXVYgPjQdNw2utDqyZahvBUXRJM88I\nE699s5h4jezUVI6Vpl0vjJhLM91jTdod5ZGkmCRiZQHHStOu6AWGpH4yABwKXElM5/kAcZNypUHf\ndYnyrFuJRbJ+R8zZP8LE0aB0W5UYqR9I2muP6Ri9l6R6Q8T1Zw/gaGAZcU27HHhR0qcC/AZ4ELgl\n6Vfv5cAPgKXJsfcS65i8uMMYhmlcJ74B8Cngr8AK4C7gEuBNHRw7CJwO3E7c33Z9cq51Uv1GaP7t\n5Srg26m2g4AriPf4ILAE+A6wSZNzSOM4Yi517nRgf+BM4FvEfPtvAX4J7AecX9f3TOAVwDnAhcDW\nwNnE/6k1GgmvbzsW+DjwH8CBde2/zeE9SNJkfJoYzFtIrNj9AeDnwCHA8cAJxDXyTcAxwA3Ad5Nj\n5wGziST3/4CnELOw/Qp4CZHUZzU7Oe4ZxPX260TZys7Aq4gPAs1sSSTPTwC+AVyXxPFR4sPGSxk/\nyt7q28v6fW8l3uOviWv4CuIDwSuATYkPDpKkSagwfpT6tcn2Ial+s4DfEyNBNa9M+n4z1fcVSXv6\na9UR2o+iS1IRhohr0R8YP5j36qT9X0QyXLMGMWpeP5DQaJa1zYA7mbiQ4AidzcryjaTt7Q3OXb+q\neaNjv5u0/WfquM8m7Qe3iadmFXBK3fbZwHKsRtAU+I9H6syBROnKj4mvJGuPOcBPiK9Ft036vjp5\nTs809DNiWlBJ6jfHM/5+mNoo9++AP9a1/4sYrHhaXdtDdT+vD2xMJLVXALtOIpbViG8vrwFObrC/\n1Qj3asA+RMw/T+07LonrtZOICSIpXw/Ym/EfDqSOWcoidWYH4mvP25vsHwU2J+oUtyJGxa9v0O9v\nwPbdCFCSumhpavve5PmGBn3vJZLvmm2I+u29gA1TfSfzzeAmRCnLTydx7KZE8vyXBvvuBW4jruGT\n8T9E3fy5wN3AImJA5gdEvbnUlom51JkB4mvXN7fok77QO6uKpLJoNrNJuxlP1idqrtcBvgRcTXz7\nuAr4GFHb3cuaXccb5U/XEzXvL00euwMnEetRvJiJH26kCUzMpc5cR9SOX07MyNLKMqL2fDsmlq50\nOlruohiS+lnt+vVSYAvgbcCpqT7/M8lz30WMbs+dxLF3Eh8Mntlg3xwi1vrSnHuS59lEqUrN1k3O\n/ygxSv6zZPsVRB39+4HDJxGvZhhrzKXOnEr893Jck/2b1/384+T5qFSfVwJPb3J8Ogl/kBiln5Mh\nRknqNbUR9XS+8XJglybHtBuUWAWcQYxOH9ymb6NjzyduWN0rte8jxHX3nLq2vyXPL0v1/UCDczea\nEvFPybPXcnXEEXOpMz8i5qs9nLigX0CM2jwFeAFRQ7lN0venxBy97yAu1L8iahbfCfwvsFOD86dv\nFPod8B5i5oGfEjdUXUaMxktSr6td035D1G1/gbhJ/h/ESPeBRFlLJ9fDRo4m5lY/mUjyL02Oew7x\njWWrdR8+RiTa5xLX2CVEqckbibrw+pH9M4iR/ROJgZV7idlc6mvoa36R7P8NcDMxyj5EfBg4vYP3\nJElqokKM9KQv7gcS9ZL3EXPULgXOAt6Q6rcuUU95GzEjwWXEV7pnMfEmoG8zsU5zAPgccXF/rEks\nktRtQ8T1p9FiQOnpAmvS17SdiNKOe4D7iYXZXtSgX6NjAeYnbelFgjYEPkOUGj5CDJYsAl7fwbGD\nwGnEDf2PEPXhxxLrU6TtQiTbK4hSmBOS106//7cTyfmtyTlvIWbt2r3BOSVJPeBqYoovSZKkcawx\nl7qj0ajLq4gbjn45zbFIkqQ+4AT4UnccR9RRXkx8dTuXuElpefLzLcWFJkmSJM0cryBqHe8kps+6\nlVgGutkUW5IkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkKathYrXB2uOFhUaTzcOMxX1xwbFIUk9a\nvegAJEmZHUksP/73ZHsusC+xnPmNRQXVxjzi/3O+BIwWHIskSZI0JcPEiPNTU+1DSfuLpzmeyVgG\nXFR0EJLUi1YrOgBJUm5czVmS+piJuST1t2HglOTnixmr4/42Ud6yCnh7k2P/AlxXt10FbgC2As4D\nlgP3AWcnbWkDwKHAlcA/gQeI0fDK5N6KJEmS1B+GmVjKshNwQtL+SeCA5LErMAu4Bfhtg3M9Pznm\nI3VtFwN3EHXqPwDeDXyRuHHzFmDz1Dm+AzwGnAEcBryfSNL/Bby6yXtYhqUskiRJ6nPDZK8x/1Sy\nb4dU+0nAo8AT69qqSd8vpvrWRt6Pr2t7bdJ2SKrvLOD3wNIm72EZJuaS1JClLJJUbicRs6DUJ9Dr\nAW8Cfgbcluo/Cnw61XYuMQPMvnVtBxKlKz8GNql7zAF+AgwC2+bxBiRppjAxl6RyWwZcCLyVsSly\n3wisD5zcoP9yopwl7a9EKcs6yfYOwBOA25P+9Y/5RIKfLn2RJLXgPOaSVH4nAmcC+xA3ch4C3Apc\nkPE89fOPDwB3Am9u0f8vGc8vSTOaibkk9b92C/acR4xkH0Ikyy8kylVWNeg7hxjpvj3VvkNyjhXJ\n9nXAK4HLiRlZJElTZCmLJPW/B5PnjZvsfwwYAfYiykwAvtXifB9Jbb8W2I6oNa85lfj/kOOanMMy\nFknKyBFzSep/VxCj3/8NbESMYC9N2mtOAj4E7E/MvrKkybnuAvYDngQsAp5GTIV4GzErTM2PiLnS\nDwd2Jspi7gKeArwA2CZ5SJIkSaUzTOPpEgEOIspUHkn6nNKgz4XJvrc0OX+VSOgHidHx+5LHOcDW\nTY45EPh10m9FcvxZwBua9F+G0yVKkiSpzw0TifVcYmrCWRmP/ylwN7BWk/1Vms8/PlUbEzHfhIm5\nJDVkjbkk9Y/aTZ5/JG7E3DXDsdsSNebfIUbVp9s/iJifUsBrS1JfsMZckvrHacAlddt/7uCYXYkZ\nVd4LPAx8oU3/gcmF1tZejI3w39ul15AkSZJ61reBlUxcubORi+leKYskSZIkSZIkSZIkSZIkSZIk\nSZIkSZIkSZIkSZIkSZIkqb/8f6cAqbY5l0pkAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6ced990>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Split the classes up so we can set colors, size, labels\n",
      "fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEE5'))\n",
      "ax.grid(color='grey', linestyle='solid')\n",
      "cond = dataframe['type'] == 'malicious'\n",
      "evil = dataframe[cond]\n",
      "legit = dataframe[~cond]\n",
      "plt.scatter(legit['length'], legit['entropy'], s=140, c='#aaaaff', label='Legit', alpha=.7)\n",
      "plt.scatter(evil['length'], evil['entropy'], s=40, c='r', label='Injections', alpha=.3)\n",
      "plt.legend()\n",
      "plt.xlabel('SQL Statement Length')\n",
      "plt.ylabel('SQL Statement Entropy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.text.Text at 0x74843d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kWUalavubxj8KvV7PP/7xPGlpafzwwyo+/vhL6urqcHV1YdiwATz88Cz8/Zuv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\n9v8Bk/ORc6NGZYgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x74b7950>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Split the classes up so we can set colors, size, labels\n",
      "fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEE5'))\n",
      "ax.grid(color='grey', linestyle='solid')\n",
      "plt.scatter(legit['malicious_g'], legit['legit_g'], s=140, c='#aaaaff', label='Legit', alpha=.7)\n",
      "plt.scatter(evil['malicious_g'], evil['legit_g'], s=40, c='r', label='Injections', alpha=.3)\n",
      "plt.legend()\n",
      "plt.ylabel('Legit SQL G-Test Score')\n",
      "plt.xlabel('Malicious SQL G-Test Score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<matplotlib.text.Text at 0x7490450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kOuXJnMQiC3C+y3GnnDpCCCGEEBatdbduqFu3ZmdSEhuuX+eSjw8hjzyCi4tL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\nERERETnS/X93NURbmBpm5gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x748fad0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# In preparation for using scikit learn we're just going to use\n",
      "# some handles that help take us from pandas land to scikit land\n",
      "\n",
      "# List of feature vectors (scikit learn uses 'X' for the matrix of feature vectors)\n",
      "X = dataframe.as_matrix(['length', 'entropy','legit_g','malicious_g'])\n",
      "\n",
      "# Labels (scikit learn uses 'y' for classification labels)\n",
      "y = np.array(dataframe['type'].tolist())  # Yes, this is weird but it needs \n",
      "                                            # to be an np.array of strings"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "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 = sklearn.ensemble.RandomForestClassifier(n_estimators=20) # Trees in the forest"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "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, X, y, cv=10, n_jobs=4)\n",
      "print scores"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.99784173  0.99784173  1.          0.99784173  0.99856115  0.99784017\n",
        "  0.99640029  0.99856012  0.99784017  0.99784017]\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Wow 99% accurate! There is an issue though...\n",
      "# Recall that we have ~13k 'malicious SQL statements and\n",
      "# we only have about 1k 'legit' SQL statements, so we dive \n",
      "# in a bit and look at the predictive performance more deeply.\n",
      "\n",
      "# Train on a 80/20 split\n",
      "from sklearn.cross_validation import train_test_split\n",
      "X_train, X_test, y_train, y_test, index_train, index_test = train_test_split(X, y, dataframe.index, test_size=0.2)\n",
      "clf.fit(X_train, y_train)\n",
      "y_pred = clf.predict(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now plot the results of the 80/20 split in a confusion matrix\n",
      "from sklearn.metrics import confusion_matrix\n",
      "labels = ['legit', 'malicious']\n",
      "cm = confusion_matrix(y_test, y_pred, labels)\n",
      "\n",
      "def plot_cm(cm, labels):\n",
      "    \n",
      "    # Compute percentanges\n",
      "    percent = (cm*100.0)/np.array(np.matrix(cm.sum(axis=1)).T)  # Derp, I'm sure there's a better way\n",
      "    \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 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')\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()\n",
      "\n",
      "plot_cm(cm, labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Confusion Matrix Stats\n",
        "legit/legit: 96.59% (170/176)\n",
        "legit/malicious: 3.41% (6/176)\n",
        "malicious/legit: 0.08% (2/2603)\n",
        "malicious/malicious: 99.92% (2601/2603)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9jqNi9ncL/f2IW/8kkuY4YJ+Y46MBeCVWOt8uQR5FRGpLLpd8yQCVlCv3IHAb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\niEi2/H/SkmhZBR86hgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x69f2f50>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: right; padding:10px;\"><img src=\"files/images/sad_party.jpg\" width=\"200px\"></div>\n",
      "<font size=4>Yea! Those scores look totally awesome, lets have a Party! But later we realize that if your system is processing 1 Million sql statements that with the predictive performance above you'll get tens of thousands of false positives, so now it's a Sad Party :(\n",
      "<br><br>\n",
      "But our Mom said we were still cool.. so we're going to exercise another nice feature. Most of the machine learning algorithm in scikit learn have a companion function to the normal 'predict' function... called 'predict_proba' where the model will compute the probability of that class matching based on various metrics. For instance, random forest bases the probability function on how many of the trees in the forest voted one way or the other.. so a probability of .7 means that 70% of the trees voted one way and the other 30% voted the other way.</font>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Compute the precition probabilities and use them to mimimize our false positives\n",
      "# Note: This is simply a trade off, it means we'll miss a few of the malicious\n",
      "# ones but typically false alarms are a death blow to any new 'fancy stuff' so\n",
      "# we definitely want to mimimize the false alarms.\n",
      "y_probs = clf.predict_proba(X_test)[:,1]\n",
      "thres = .9 # This can be set to whatever you'd like\n",
      "y_pred[y_probs<thres] = 'legit'\n",
      "y_pred[y_probs>=thres] = 'malicious'\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",
        "legit/legit: 98.30% (173/176)\n",
        "legit/malicious: 1.70% (3/176)\n",
        "malicious/legit: 0.38% (10/2603)\n",
        "malicious/malicious: 99.62% (2593/2603)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xzlF3hbbt4daLPemDbXsWOqmCrYiIZE9lSrY9gaOxO6y9F9rewa03eI5ZH0nj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\nPP8fuNvZumi94FIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x69eac50>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We can also look at what features the learning algorithm thought were the most important\n",
      "importances = zip(['length', 'entropy', 'legit_g', 'malicious_g'], clf.feature_importances_)\n",
      "importances\n",
      "\n",
      "# From the list below we see our feature importance scores. There's a lot of feature selection,\n",
      "# sensitivity study, etc stuff that you could do if you wanted at this point."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "[('length', 0.034947270077838898),\n",
        " ('entropy', 0.081305411355911961),\n",
        " ('legit_g', 0.63638493679534702),\n",
        " ('malicious_g', 0.247362381770902)]"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now were going to just do some post analysis on how the ML algorithm performed.\n",
      "# Lets look at the legit samples that were misclassified as malicious\n",
      "test_set = dataframe.ix[index_test]\n",
      "test_set['pred'] = y_pred\n",
      "misclassified = test_set[(test_set['type']=='legit') & (test_set['pred']=='malicious')]\n",
      "misclassified.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>raw_sql</th>\n",
        "      <th>type</th>\n",
        "      <th>parsed_sql</th>\n",
        "      <th>sequences</th>\n",
        "      <th>length</th>\n",
        "      <th>entropy</th>\n",
        "      <th>malicious_g</th>\n",
        "      <th>legit_g</th>\n",
        "      <th>pred</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>13606</th>\n",
        "      <td> create table Purchase (pid int primary key, pr...</td>\n",
        "      <td> legit</td>\n",
        "      <td>             [DDL, Keyword, Function, Punctuation]</td>\n",
        "      <td> [('DDL',), ('Keyword',), ('Function',), ('Punc...</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4.400948</td>\n",
        "      <td>-174.930469</td>\n",
        "      <td> 513.570637</td>\n",
        "      <td> malicious</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13605</th>\n",
        "      <td> create table Product (pid int primary key, pna...</td>\n",
        "      <td> legit</td>\n",
        "      <td>             [DDL, Keyword, Function, Punctuation]</td>\n",
        "      <td> [('DDL',), ('Keyword',), ('Function',), ('Punc...</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4.137866</td>\n",
        "      <td>-174.930469</td>\n",
        "      <td> 513.570637</td>\n",
        "      <td> malicious</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13495</th>\n",
        "      <td> SELECT dept, number, SUBSTR(title, 1, 12) AS s...</td>\n",
        "      <td> legit</td>\n",
        "      <td> [DML, Identifier, Punctuation, Builtin, Punctu...</td>\n",
        "      <td> [('DML',), ('Identifier',), ('Punctuation',), ...</td>\n",
        "      <td> 8</td>\n",
        "      <td> 4.699688</td>\n",
        "      <td> -20.133315</td>\n",
        "      <td> 353.217738</td>\n",
        "      <td> malicious</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "                                                 raw_sql   type  \\\n",
        "13606  create table Purchase (pid int primary key, pr...  legit   \n",
        "13605  create table Product (pid int primary key, pna...  legit   \n",
        "13495  SELECT dept, number, SUBSTR(title, 1, 12) AS s...  legit   \n",
        "\n",
        "                                              parsed_sql  \\\n",
        "13606              [DDL, Keyword, Function, Punctuation]   \n",
        "13605              [DDL, Keyword, Function, Punctuation]   \n",
        "13495  [DML, Identifier, Punctuation, Builtin, Punctu...   \n",
        "\n",
        "                                               sequences  length   entropy  \\\n",
        "13606  [('DDL',), ('Keyword',), ('Function',), ('Punc...       4  4.400948   \n",
        "13605  [('DDL',), ('Keyword',), ('Function',), ('Punc...       4  4.137866   \n",
        "13495  [('DML',), ('Identifier',), ('Punctuation',), ...       8  4.699688   \n",
        "\n",
        "       malicious_g     legit_g       pred  \n",
        "13606  -174.930469  513.570637  malicious  \n",
        "13605  -174.930469  513.570637  malicious  \n",
        "13495   -20.133315  353.217738  malicious  \n",
        "\n",
        "[3 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Discussion for how to use the resulting models.\n",
      "# Typically Machine Learning comes in two phases\n",
      "#    - Training of the Model\n",
      "#    - Evaluation of new observations against the Model\n",
      "# This notebook is about exploration of the data and training the model.\n",
      "# After you have a model that you are satisfied with, just 'pickle' it\n",
      "# at the end of the your training script and then in a separate\n",
      "# evaluation script 'unpickle' it and evaluate/score new observations\n",
      "# coming in (through a file, or ZeroMQ, or whatever...)\n",
      "#\n",
      "# In this case we'd have to pickle the RandomForest classifier.\n",
      "# See 'test_it' below for how to use them in evaluation mode.\n",
      "\n",
      "\n",
      "# test_it shows how to do evaluation, also fun for manual testing below :)\n",
      "def test_it(sql):\n",
      "    parsed_sql = parse_it(sql)\n",
      "    ngram_list = ngrams(parsed_sql, 3)\n",
      "    malicious_g = g_aggregate(ngram_list, 'malicious_g')\n",
      "    legit_g = g_aggregate(ngram_list, 'legit_g')\n",
      "    _X = [len(parsed_sql), entropy(sql), legit_g, malicious_g]\n",
      "    print '%-40s: %s' % (sql, clf.predict(_X)[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_it('select * from employees')\n",
      "test_it(\"'; exec master..xp_cmdshell\")\n",
      "test_it(\"'any 'x'='x'\")\n",
      "test_it('from dorseys mom xp_cmdshell biache')\n",
      "test_it('select * from your_mom')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "select * from employees                 : legit\n",
        "'; exec master..xp_cmdshell             : malicious\n",
        "'any 'x'='x'                            : malicious\n",
        "from dorseys mom xp_cmdshell biache     : malicious\n",
        "select * from your_mom                  : legit\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Conclusions:\n",
      "The combination of IPython, Pandas and Scikit Learn let us pull in some junky SQL data, clean it up, plot it, understand it and slap it with some machine learning!\n",
      "\n",
      "Clearly a lot more formality could be used, plotting learning curves, adjusting for overfitting, feature selection, on and on... there are some really great machine learning resources that cover this deeper material. In particular we highly recommend the work and presentations of Olivier Grisel at INRIA Saclay. http://ogrisel.com/\n",
      "\n",
      " - W. G.J. Halfond, J. Viegas, and A. Orso, \"A Classi\ufb01cation of SQL Injection Attacks and Countermeasure\",[http://www-bcf.usc.edu/~halfond/papers/halfond06issse.pdf]\n",
      " - A. K. Baranwa1, \"Approaches to detect SQL injection and XSS in web applications,\" 2012. [http://blogs.ubc.ca/computersecurity/files/2012/04/ABaranwal_ApproachesToDetectSQLinjection_XSSinWebApplication.pdf]"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}