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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: right; margin: 0px 0px 0px 30px\"><img src=\"files/images/workbench.jpg\" width=\"400px\"></div>\n",
      "# Workbench: \n",
      "A light-weight and flexible approach to task management, execution and pipelining.\n",
      "## Adoption of Workbench:\n",
      "Open source tools have 3 primary components that are critical for adoption:\n",
      "\n",
      "  - ### How easy is it to get the system up and running?\n",
      "    - git clone https://github.com/SuperCowPowers/workbench.git\n",
      "    - Go through the 'Workbench Dependencies' section in the readme and about an hour later you should be done... \n",
      "    - So not great but not horrible either\n",
      " \n",
      "  - ### How easy is it to use?\n",
      "    - ZeroRPC has Python, Node.js, or CLI interfaces.See [ZeroRPC](http://zerorpc.dotcloud.com/).\n",
      "    <pre>\n",
      "    import zerorpc\n",
      "    c = zerorpc.Client()\n",
      "    c.connect(\"tcp://127.0.0.1:4242\")\n",
      "    with open('evil.pcap','rb') as f:\n",
      "        md5 = c.store_sample(f.read(), 'evil.pcap', 'pcap')\n",
      "    print c.work_request('pcap_meta', md5)\n",
      "    </pre>\n",
      "    - Output from above 'client':\n",
      "    <pre>\n",
      "    {'pcap_meta': {'encoding': 'binary',\n",
      "     'file_size': 54339570,\n",
      "     'file_type': 'tcpdump (little-endian) - version 2.4 (Ethernet, 65535)',\n",
      "     'filename': 'evil.pcap',\n",
      "     'import_time': '2014-02-08T22:15:50.282000Z',\n",
      "     'md5': 'bba97e16d7f92240196dc0caef9c457a',\n",
      "     'mime_type': 'application/vnd.tcpdump.pcap'}}\n",
      "    </pre>\n",
      "  - ### How easy is it for me to put my own code into it?\n",
      "    - Below is the listing of the code for the 'strings' worker: Each worker needs to provide:\n",
      "        - dependencies (which other workers do you want as input)\n",
      "        - An execute method that takes a python dictionary as input and returns a python dictionary as output.\n",
      "        - That's it...\n",
      "<img src=\"files/images/strings_worker.png\" width=\"800px\" align=\"center\">\n",
      "\n",
      "## Lets start up the workbench server...\n",
      "Run the workbench server (from somewhere, for the demo we're just going to start a local one)\n",
      "<pre>\n",
      "$ workbench_server\n",
      "</pre>\n",
      "\n",
      "#### Okay so when the server starts up, it autoloads any worker plugins in the server/worker directory and dynamically monitors the directory, if a new python file shows up, it's validated as a properly formed plugin and if it passes is added to the list of workers."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Lets start to interact with workbench, please note there is NO specific client to workbench,\n",
      "# Just use the ZeroRPC Python, Node.js, or CLI interfaces.\n",
      "import zerorpc\n",
      "c = zerorpc.Client()\n",
      "c.connect(\"tcp://127.0.0.1:4242\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "[None]"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: left; margin: 0px 0px 0px 0px\"><img src=\"files/images/confused.jpg\" width=\"350px\"></div>\n",
      "\n",
      "## So I'm confused what am I suppose to do with workbench? \n",
      "<br>\n",
      "<font size=4> Workbench is often confusing for new users (we're trying to work on that). Please see our github repository https://github.com/SuperCowPowers/workbench for the latest documentation and notebooks examples (the notebook examples can really help). New users can start by typing **c.help()** after they connect to workbench.</font>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# I forgot what stuff I can do with workbench\n",
      "print c.help()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to Workbench: Here's a list of help commands:\n",
        "\t - Run c.help_basic() for beginner help\n",
        "\t - Run c.help_commands() for command help\n",
        "\t - Run c.help_workers() for a list of workers\n",
        "\t - Run c.help_advanced() for advanced help\n",
        "\n",
        "See https://github.com/SuperCowPowers/workbench for more information\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print c.help_basic()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Workbench: Getting started...\n",
        "\t - 1) $ print c.help_commands() for a list of commands\n",
        "\t - 2) $ print c.help_command('store_sample') for into on a specific command\n",
        "\t - 3) $ print c.help_workers() for a list a workers\n",
        "\t - 4) $ print c.help_worker('meta') for info on a specific worker\n",
        "\t - 5) $ my_md5 = c.store_sample(...)\n",
        "\t - 6) $ output = c.work_request('meta', my_md5)\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# STEP 1:\n",
      "# Okay get the list of commands from workbench\n",
      "print c.help_commands()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Workbench Commands:\n",
        "\tadd_node(node_id, name, labels)\n",
        "\tadd_rel(source_id, target_id, rel)\n",
        "\tclear_db()\n",
        "\tclear_graph_db()\n",
        "\tget_datastore_uri()\n",
        "\tget_sample(md5)\n",
        "\tget_sample_set(md5)\n",
        "\tget_sample_window(type_tag, size)\n",
        "\thas_node(node_id)\n",
        "\thave_sample(md5)\n",
        "\thelp()\n",
        "\thelp_advanced()\n",
        "\thelp_basic()\n",
        "\thelp_command(command)\n",
        "\thelp_commands()\n",
        "\thelp_worker(worker)\n",
        "\thelp_workers()\n",
        "\tindex_sample(md5, index_name)\n",
        "\tindex_worker_output(worker_class, md5, index_name, subfield)\n",
        "\tsearch(index_name, query)\n",
        "\tstore_sample(input_bytes, filename, type_tag)\n",
        "\tstore_sample_set(md5_list)\n",
        "\twork_request(worker_class, md5, subkeys=None)\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# STEP 2:\n",
      "# Lets gets the infomation on a specific command 'store_sample'\n",
      "print c.help_command('store_sample')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " Command: store_sample(input_bytes, filename, type_tag) \n",
        " Store a sample into the DataStore.\n",
        "            Args:\n",
        "                filename: name of the file (used purely as meta data not for lookup)\n",
        "                input_bytes: the actual bytes of the sample e.g. f.read()\n",
        "                type_tag: ('exe','pcap','pdf','json','swf', or ...)\n",
        "            Returns:\n",
        "                the md5 of the sample\n",
        "        \n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# STEP 3:\n",
      "# Now lets get infomation about the dynamically loaded workers (your site may have many more!)\n",
      "# Next to each worker name is the list of dependences that worker has declared\n",
      "print c.help_workers()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Workbench Workers:\n",
        "\tjson_meta ['sample', 'meta']\n",
        "\tlog_meta ['sample', 'meta']\n",
        "\tmeta ['sample']\n",
        "\tmeta_deep ['sample', 'meta']\n",
        "\tpcap_bro ['sample']\n",
        "\tpcap_graph ['pcap_bro']\n",
        "\tpcap_graph_0_1 ['pcap_bro']\n",
        "\tpcap_http_graph ['pcap_bro']\n",
        "\tpe_classifier ['pe_features', 'pe_indicators']\n",
        "\tpe_deep_sim ['meta_deep']\n",
        "\tpe_features ['sample']\n",
        "\tpe_indicators ['sample']\n",
        "\tpe_peid ['sample']\n",
        "\tstrings ['sample']\n",
        "\tswf_meta ['sample', 'meta']\n",
        "\tunzip ['sample']\n",
        "\turl ['strings']\n",
        "\tview ['meta']\n",
        "\tview_customer ['meta']\n",
        "\tview_log_meta ['log_meta']\n",
        "\tview_meta ['meta']\n",
        "\tview_pcap ['pcap_bro']\n",
        "\tview_pcap_details ['view_pcap']\n",
        "\tview_pdf ['meta', 'strings']\n",
        "\tview_pe ['meta', 'strings', 'pe_peid', 'pe_indicators', 'pe_classifier', 'pe_disass']\n",
        "\tview_zip ['meta', 'unzip']\n",
        "\tvt_query ['meta']\n",
        "\tyara_sigs ['sample']\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# STEP 4:\n",
      "# Lets gets the infomation about the meta worker\n",
      "print c.help_worker('meta')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " Worker: meta ['sample']\n",
        "\t This worker computes meta data for any file type. \n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: right; margin: 0px 30px 0px 0px\"><img src=\"files/images/feeling_awesome.jpg\" width=\"300px\"></div>\n",
      "# Alright, are we feeling awesome yet? \n",
      "## Let do some stuff\n",
      "<font size=4> Workbench has lots of samples that you can try out (some of them are malicious, be careful!). So you can grab a PE file, a PCAP, or a PDF file and throw it in!\n",
      "</font>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# STEP 5:\n",
      "# Okay when we load up a file, we get the md5 back\n",
      "filename = '../data/pe/bad/0cb9aa6fb9c4aa3afad7a303e21ac0f3'\n",
      "with open(filename,'rb') as f:\n",
      "    my_md5 = c.store_sample(f.read(), filename, 'exe')\n",
      "print my_md5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0cb9aa6fb9c4aa3afad7a303e21ac0f3\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# STEP 6:\n",
      "# Run a worker on my sample\n",
      "output = c.work_request('meta', my_md5)\n",
      "output"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "{'meta': {'customer': 'BearTron',\n",
        "  'encoding': 'binary',\n",
        "  'file_size': 20480,\n",
        "  'file_type': 'PE32 executable (GUI) Intel 80386, for MS Windows',\n",
        "  'filename': '../data/pe/bad/0cb9aa6fb9c4aa3afad7a303e21ac0f3',\n",
        "  'import_time': '2014-06-10T20:48:15.321000Z',\n",
        "  'length': 20480,\n",
        "  'md5': '0cb9aa6fb9c4aa3afad7a303e21ac0f3',\n",
        "  'mime_type': 'application/x-dosexec',\n",
        "  'type_tag': 'exe'}}"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: left; margin: 0px 30px 0px 0px\"><img src=\"files/images/feeling_more_awesome.jpg\" width=\"350px\"></div>\n",
      "# Alright, now it's time to get fabulous!\n",
      "### Let check out some of the other workers\n",
      "<font size=4> We saw a bunch of workers for PE Files, so lets look at the help for those and start taking it up a notch!\n",
      "</font>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Lets see what view_pe does\n",
      "print c.help_worker('view_pe')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " Worker: view_pe ['meta', 'strings', 'pe_peid', 'pe_indicators', 'pe_classifier', 'pe_disass']\n",
        "\t Generates a high level summary view for PE files that incorporates a large set of workers \n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Okay lets give it a try\n",
      "c.work_request('view_pe', my_md5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "{'view_pe': {'classification': 'Evil!',\n",
        "  'customer': 'BearTron',\n",
        "  'disass': 'plugin_failed',\n",
        "  'encoding': 'binary',\n",
        "  'file_size': 20480,\n",
        "  'file_type': 'PE32 executable (GUI) Intel 80386, for MS Windows',\n",
        "  'filename': '../data/pe/bad/0cb9aa6fb9c4aa3afad7a303e21ac0f3',\n",
        "  'import_time': '2014-06-10T20:48:15.321000Z',\n",
        "  'indicators': [{'attributes': ['findwindowexa', 'findwindowa'],\n",
        "    'category': 'ANTI_DEBUG',\n",
        "    'description': 'Imported symbols related to anti-debugging',\n",
        "    'severity': 3},\n",
        "   {'category': 'MALFORMED', 'description': 'Checksum of Zero', 'severity': 1},\n",
        "   {'category': 'MALFORMED',\n",
        "    'description': 'Reported Checksum does not match actual checksum',\n",
        "    'severity': 2},\n",
        "   {'attributes': ['sendmessagea'],\n",
        "    'category': 'COMMUNICATION',\n",
        "    'description': 'Imported symbols related to network communication',\n",
        "    'severity': 1},\n",
        "   {'attributes': ['getmodulehandlea', 'getstartupinfoa'],\n",
        "    'category': 'PROCESS_MANIPULATION',\n",
        "    'description': 'Imported symbols related to process manipulation/injection',\n",
        "    'severity': 3},\n",
        "   {'attributes': ['getsystemmetrics'],\n",
        "    'category': 'PROCESS_SPAWN',\n",
        "    'description': 'Imported symbols related to spawning a new process',\n",
        "    'severity': 2}],\n",
        "  'length': 20480,\n",
        "  'md5': '0cb9aa6fb9c4aa3afad7a303e21ac0f3',\n",
        "  'mime_type': 'application/x-dosexec',\n",
        "  'peid_Matches': ['Microsoft Visual C++ v6.0'],\n",
        "  'type_tag': 'exe'}}"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# The Workbench framework is client/server so here's what just happened...\n",
      "<br>\n",
      "<div style=\"margin: 0px 30px 0px 0px\"><img src=\"files/images/client_server.png\" width=\"900px\"></div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Okay, that worker needed the output of pe_features and pe_indicators\n",
      "# so what happened? The worker has a dependency list and workbench\n",
      "# recursively satisfies that dependency list.. this is powerful because\n",
      "# when we're interested in one particular analysis we just want to get\n",
      "# the darn thing without having to worry about a bunch of details\n",
      "\n",
      "# Well lets do this for a bunch of files!\n",
      "import os\n",
      "file_list = [os.path.join('../data/pe/bad', child) for child in os.listdir('../data/pe/bad')]\n",
      "working_set = []\n",
      "for filename in file_list:\n",
      "    with open(filename,'rb') as f:\n",
      "        md5 = c.store_sample(f.read(), filename, 'exe')\n",
      "        results = c.work_request('pe_classifier', md5)\n",
      "        working_set.append(md5)\n",
      "        print 'Results: %s' % (results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '033d91aae8ad29ed9fbb858179271232'}}\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '0cb9aa6fb9c4aa3afad7a303e21ac0f3'}}\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '0e882ec9b485979ea84c7843d41ba36f'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Benign', 'md5': '0e8b030fb6ae48ffd29e520fc16b5641'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Benign', 'md5': '0eb9e990c521b30428a379700ec5ab3e'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '127f2bade752445b3dbf2cf2ea75c201'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '139385a91b9bca0833bdc1fa77e42b91'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '13dcc5b4570180118eb65529b77f6d89'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '1cac80a2147cd8f3860547e43edcaa00'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '1cea13cf888cd8ce4f869029f1dbb601'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '1d733a9e3e571ce5f5f633a0cfd3d5f0'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2058c50de5976c67a09dfa5e0e1c7eb5'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d012cba541c22fb7250975d5ad0d065'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d015553c7388e4d78f05b24aba0819c'}}\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d017ff228c39a0a727586b33f8168b0'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d09133abb48e1c7f3f5c8f8ced8fef4'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d094b6c69020091b68d1bcf5d11fa4b'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d095091983dd0bf6ab7c0bb6dd695f9'}}\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d09546831b17d2cc0583362b6d312ae'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d099171876f7301c155bd775fff2b6a'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Benign', 'md5': '2d09a573b0e9d02a9fa47b16e9c01a48'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d09b5768e3617523d8afa110361919c'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d09b8d9852c3176259915e3509bcbd1'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d09ca902990545fec9ac190b0338b50'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d09cb38c268aa9297e5a7f27e677267'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d09cc92bbe29d96bb3a91b350d1725f'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '2d09e4aff42aebac87ae2fd737aba94f'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '32b24e73cfc3ac4c43f1926f8935e438'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '4ed28b6207560f127d267de639a4e1bf'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '505804ec7c7212a52ec85e075b91ed84'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '60a83c049e135cc199138c1f8861437c'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '69f9633fa6fd5dc1fd917cb435bba8ad'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '79f5e1af9fdb92476045989bda7515c7'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '7f313447b887b078215617fbed1a34a1'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '8006782bdf703e2f3fdf1d1650f45ffd'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '86714940f491bc38c2e842e80c7f778e'}}\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '987bd46899b2a9493e6dec051edcb66c'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '9cd3d7b1b0f2aea5950cbf7d97776f2f'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '9ceccd9f32cb2ad0b140b6d15d8993b6'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': '9e42ff1e6f75ae3e60b24e48367c8f26'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': 'a7b0a9067d8292b252d741e6fae17cd9'}}\n",
        "Results: {'pe_classifier': {'classification': 'Benign', 'md5': 'afddc552b31a8f2438768c73674bf29e'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': 'b681485cb9e0cad73ee85b9274c0d3c2'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': 'bf1249a258cbcccec0f1b4ea1e9451a1'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': 'c8c54ac7e827056174762c68db84534f'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': 'cc113aa59c04b17e7cb832fc417f104d'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Benign', 'md5': 'd94da41e7e809f7366971b3b50f8ef68'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': 'e9a6c83826deacfbc2281b6c7e401694'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': 'ea5d95c96a23b21b9038f03b91955c18'}}\n",
        "Results: {'pe_classifier': {'classification': 'Evil!', 'md5': 'f6190648c2efb764ae1d73b0e9a4fd13'}}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We just ran the classifer on 50 files and you'll note that we ONLY got back the\n",
      "# information we ask for. On a large amount of files (100k or greater) if you don't\n",
      "# have a granular system, something this easy WILL NOT BE POSSIBLE! (dramatic enough?)\n",
      "\n",
      "# So lets look at the features going into the classifier (btw the classifier is currently a TOY EXAMPLE)\n",
      "c.work_request('pe_features', md5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "{'pe_features': {'dense_features': {'check_sum': 0,\n",
        "   'compile_date': 1074901182,\n",
        "   'datadir_IMAGE_DIRECTORY_ENTRY_BASERELOC_size': 2348796035,\n",
        "   'datadir_IMAGE_DIRECTORY_ENTRY_EXPORT_size': 0,\n",
        "   'datadir_IMAGE_DIRECTORY_ENTRY_IAT_size': 0,\n",
        "   'datadir_IMAGE_DIRECTORY_ENTRY_IMPORT_size': 20,\n",
        "   'datadir_IMAGE_DIRECTORY_ENTRY_RESOURCE_size': 262,\n",
        "   'debug_size': 0,\n",
        "   'export_size': 0,\n",
        "   'generated_check_sum': 62500,\n",
        "   'iat_rva': 86510,\n",
        "   'major_version': 0,\n",
        "   'minor_version': 57,\n",
        "   'number_of_bound_import_symbols': 0,\n",
        "   'number_of_bound_imports': 0,\n",
        "   'number_of_export_symbols': 0,\n",
        "   'number_of_import_symbols': 0,\n",
        "   'number_of_imports': 0,\n",
        "   'number_of_rva_and_sizes': 10,\n",
        "   'number_of_sections': 3,\n",
        "   'pe_char': 33167,\n",
        "   'pe_dll': 0,\n",
        "   'pe_driver': 0,\n",
        "   'pe_exe': 1,\n",
        "   'pe_i386': 1,\n",
        "   'pe_majorlink': 76,\n",
        "   'pe_minorlink': 111,\n",
        "   'pe_warnings': 1,\n",
        "   'sec_entropy_': 7.876937179741062,\n",
        "   'sec_entropy_@': 5.388003199139927,\n",
        "   'sec_entropy_data': 0,\n",
        "   'sec_entropy_ps': 5.388003199139927,\n",
        "   'sec_entropy_rdata': 0,\n",
        "   'sec_entropy_reloc': 0,\n",
        "   'sec_entropy_rsrc': 0,\n",
        "   'sec_entropy_text': 0,\n",
        "   'sec_raw_execsize': 6984,\n",
        "   'sec_rawptr_': 512,\n",
        "   'sec_rawptr_@': 16,\n",
        "   'sec_rawptr_data': 0,\n",
        "   'sec_rawptr_ps': 16,\n",
        "   'sec_rawptr_rsrc': 0,\n",
        "   'sec_rawptr_text': 0,\n",
        "   'sec_rawsize_': 5992,\n",
        "   'sec_rawsize_@': 496,\n",
        "   'sec_rawsize_data': 0,\n",
        "   'sec_rawsize_ps': 496,\n",
        "   'sec_rawsize_rsrc': 0,\n",
        "   'sec_rawsize_text': 0,\n",
        "   'sec_va_execsize': 86016,\n",
        "   'sec_vasize_': 36864,\n",
        "   'sec_vasize_@': 4096,\n",
        "   'sec_vasize_data': 0,\n",
        "   'sec_vasize_ps': 45056,\n",
        "   'sec_vasize_rsrc': 0,\n",
        "   'sec_vasize_text': 0,\n",
        "   'size_code': 1766614113,\n",
        "   'size_image': 90112,\n",
        "   'size_initdata': 1918988898,\n",
        "   'size_uninit': 16761,\n",
        "   'std_section_names': 0,\n",
        "   'total_size_pe': 6504,\n",
        "   'virtual_address': 4096,\n",
        "   'virtual_size': 45056,\n",
        "   'virtual_size_2': 36864},\n",
        "  'md5': 'f6190648c2efb764ae1d73b0e9a4fd13',\n",
        "  'sparse_features': {'imp_hash': 'Not found: Install pefile 1.2.10-139 or later',\n",
        "   'imported_symbols': [],\n",
        "   'pe_warning_strings': ['Error parsing section 0. PointerToRawData should normally be a multiple of FileAlignment, this might imply the file is trying to confuse tools which parse this incorrectly',\n",
        "    'Suspicious flags set for section 0. Both IMAGE_SCN_MEM_WRITE and IMAGE_SCN_MEM_EXECUTE are set. This might indicate a packed executable.',\n",
        "    'Suspicious flags set for section 1. Both IMAGE_SCN_MEM_WRITE and IMAGE_SCN_MEM_EXECUTE are set. This might indicate a packed executable.',\n",
        "    'Error parsing section 2. PointerToRawData should normally be a multiple of FileAlignment, this might imply the file is trying to confuse tools which parse this incorrectly',\n",
        "    'Suspicious flags set for section 2. Both IMAGE_SCN_MEM_WRITE and IMAGE_SCN_MEM_EXECUTE are set. This might indicate a packed executable.',\n",
        "    'Corrupt header \"IMAGE_IMPORT_DESCRIPTOR\" at file offset 494. Exception: \\'Data length less than expected header length.\\'',\n",
        "    \"Invalid relocation information. Can't read data at RVA: 0x476ffa5\"],\n",
        "   'section_names': ['ps', '', '@']}}}"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c.work_request('pe_indicators', md5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "{'pe_indicators': {'indicator_list': [{'category': 'PE_WARN',\n",
        "    'description': 'Error parsing section 0. PointerToRawData should normally be a multiple of FileAlignment, this might imply the file is trying to confuse tools which parse this incorrectly',\n",
        "    'severity': 2},\n",
        "   {'category': 'PE_WARN',\n",
        "    'description': 'Suspicious flags set for section 0. Both IMAGE_SCN_MEM_WRITE and IMAGE_SCN_MEM_EXECUTE are set. This might indicate a packed executable.',\n",
        "    'severity': 2},\n",
        "   {'category': 'PE_WARN',\n",
        "    'description': 'Suspicious flags set for section 1. Both IMAGE_SCN_MEM_WRITE and IMAGE_SCN_MEM_EXECUTE are set. This might indicate a packed executable.',\n",
        "    'severity': 2},\n",
        "   {'category': 'PE_WARN',\n",
        "    'description': 'Error parsing section 2. PointerToRawData should normally be a multiple of FileAlignment, this might imply the file is trying to confuse tools which parse this incorrectly',\n",
        "    'severity': 2},\n",
        "   {'category': 'PE_WARN',\n",
        "    'description': 'Suspicious flags set for section 2. Both IMAGE_SCN_MEM_WRITE and IMAGE_SCN_MEM_EXECUTE are set. This might indicate a packed executable.',\n",
        "    'severity': 2},\n",
        "   {'category': 'PE_WARN',\n",
        "    'description': 'Corrupt header \"IMAGE_IMPORT_DESCRIPTOR\" at file offset 494. Exception: \\'Data length less than expected header length.\\'',\n",
        "    'severity': 2},\n",
        "   {'category': 'PE_WARN',\n",
        "    'description': \"Invalid relocation information. Can't read data at RVA: 0x476ffa5\",\n",
        "    'severity': 2},\n",
        "   {'category': 'MALFORMED', 'description': 'Checksum of Zero', 'severity': 1},\n",
        "   {'category': 'MALFORMED',\n",
        "    'description': 'Reported Checksum does not match actual checksum',\n",
        "    'severity': 2},\n",
        "   {'category': 'MALFORMED',\n",
        "    'description': 'Image size does not match reported size',\n",
        "    'severity': 3},\n",
        "   {'attributes': ['ps', '', '@'],\n",
        "    'category': 'MALFORMED',\n",
        "    'description': 'Section(s) with a non-standard name, tamper indication',\n",
        "    'severity': 3},\n",
        "   {'attributes': ['PS', '@\\x00\\x0f@\\x00'],\n",
        "    'category': 'MALFORMED',\n",
        "    'description': 'Unaligned section, tamper indication',\n",
        "    'severity': 3}],\n",
        "  'md5': 'f6190648c2efb764ae1d73b0e9a4fd13'}}"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Wow, that's a lot of features... Good thing I can ask for just what I want and minimize network traffic.\n",
      "\n",
      "On another note, did we just waste some time there? Did workbench have to recompute the features? No everything done by workbench is pushed into the MongoDB backend and then if the work results for that md5 are already in the datastore the a very lightweight call is made to get the results. In fact results are never directly returned, the worker pushes into Mongo and then we pull them out and hand them to the client, that way we ^ensure^ that the bits in the datastore and the bits that you get are the exact same 'gold bits' (seems like overkill but it's important).\n",
      "## Your data - Transparent, Organized, Accessible\n",
      "<div style=\"margin: 50px 0px 0px 30px\"><img src=\"files/images/mongo_data.png\" width=\"800px\"></div>\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Another example.. I want to look at strings for different types of files (not just pe_files)\n",
      "# So we can load up a few pdfs (the pe's are already in the datastore)\n",
      "file_list = [os.path.join('../data/pdf/bad', child) for child in os.listdir('../data/pdf/bad')]\n",
      "for filename in file_list:\n",
      "    with open(filename,'rb') as f:\n",
      "        md5 = c.store_sample(f.read(), filename, 'pdf')\n",
      "        working_set.append(md5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now we rip the strings worker on them all\n",
      "for md5 in working_set:\n",
      "    result = c.work_request('strings', md5)\n",
      "    print 'results: %s' % (result['strings']['string_list'][:5]) # strings output is large so just showing the first 5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "results: ['    ', '!This program cannot be run in DOS mode.', 'Rich', '.text', '.rdata']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich3', '.text', '`.rdata', '@.data']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', '`.data', '.rsrc']\n",
        "results: ['This program must be run under Win32', 'CODE', '`DATA', '.idata', '.tls']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', '`.rdata', '@DATA']\n",
        "results: ['!This program cannot be run in DOS mode.', 'kRich', '.text', '`.rdata', '@.data']\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '.code', '`.data', '.data3']\n",
        "results: ['!This program cannot be run in DOS mode.', 'YRich', '.text', '`.rdata', '@.data']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '`.data', '.rsrc', 'blenkxr']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', '`.rdata', '@.data']\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '.data', '.rsrc', 'aitrfvl']\n",
        "results: ['This program must be run under Win32', 'ATSEC0', '`ATSEC1', '@ATSEC2', '@idata']\n",
        "results: ['This program must be run under Win32', 'CODE', '`DATA', '.idata', '.reloc']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "results: ['yrf<[LordPE]', '.text', '.text', 'ExitProcess', 'KERNEL32.dll']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', '`rdata', '.data']\n",
        "results: ['!This program cannot be run in DOS mode.', '6Rich#', '^\"+M', 'UPX0', 'UPX1']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich\"', '.text', '@.code', '`.data']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "results: ['yrf<[LordPE]', '.text', '.text', 'ExitProcess', 'KERNEL32.dll']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Riche', '.text', '.data', '.rsrc']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', '`rdata', '.data']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.data', '`.data', '`.data']\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '`.data', '.idata', '@.rsrc']\n",
        "results: ['!This program cannot be run in DOS mode.', \">O'X_!tX_!tX_!t\", '@+tN_!t', 'C/tR_!t:@2tS_!tX_ t', '@*t[_!t']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "results: ['!This program cannot be run in DOS mode.', 'RichX', 'E0TK', '.text', '`.rdata']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', '`rdata', '.data']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', '`.rdata', '@.data']\n",
        "results: ['!This program cannot be run in DOS mode.', 'UPX0', 'UPX1', '.rsrc', '3.03']\n",
        "results: ['    ', '!This program cannot be run in DOS mode.', 'Rich', '.text', '.rdata']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.data', '.pdata', '.ex_cod']\n",
        "results: ['This program must be run under Win32', 'CODE', '`DATA', '.idata', '.tls']\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '`.rdata', '@.data', '_TEXT2']\n",
        "results: ['!This program cannot be run in DOS mode.', 'ERich', 'UPX0', 'UPX1', 'UPX2']\n",
        "results: ['This program must be run under Win32', 'mnYD', 'CODE', '`DATA', '.idata']\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '`.rdata', '@.data', '_TEXT2']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.PEX', '`.PEX', 'Bome']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "results: ['!This program cannot be run in DOS mode.', '|Richv', 'UPX0', 'UPX1', '.rsrc']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', '`.rdata', '@.data']\n",
        "results: ['!This program cannot be run in DOS mode.', 'sIPE', '.text', '.data', '.rsrc']\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '.data', '.rsrc', 'mzphdwa']\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '.data', '.rsrc', 'lsicbkg']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.packed', '`.RLPack', 'a?/u']\n",
        "results: ['!This program cannot be run in DOS mode.', 'Rich', '.text', 'h.rdata', 'H.reloc']\n",
        "results: ['This program must be run under Win32', 'CODE', '`DATA', '.idata', '.tls']\n",
        "results: ['    ', '!This program cannot be run in DOS mode.', 'Rich', '.text', '.rdata']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "results: ['!This program cannot be run in DOS mode.', '.text', '.data', '.rdata', '@.bss']\n",
        "results: ['!This program cannot be run in DOS mode.', 'ssaR', \"'#K'\", '.data', '.text']\n",
        "results: ['!This program cannot be run in DOS mode.', 'BZ.`', 'Richy', 'J!NH', '.text']\n",
        "results: ['!This program cannot be run in DOS mode.', 'RichW', '.text', '.data', '.rsrc']\n",
        "results: ['.text', 'KERNEL32.DLL', 'MSVCRT.DLL', 'USER32.DLL', 'ADVAPI32.DLL']\n",
        "results: ['MZKERNEL32.DLL', 'LoadLibraryA', 'GetProcAddress', '^]YF', 'W8mu']\n",
        "results: ['%PDF-1.6', '52 0 obj<</Length 51252212/Root 1 0 R/Info 3 0 R%/F/W[1 2 1]/Index[5 1 7 1 9 4 23 4 50 3]>>stream', '/Filter/FlateDecode/W[1 2 1]/Index[5 1 7 1 9 4 23 4 50 3]>>stream', 'bbb0b`b```', '310Z']"
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       "stream": "stdout",
       "text": [
        "\n",
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        "\n",
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       "stream": "stdout",
       "text": [
        "\n",
        "results: ['%PDF-1.6', '9 0 obj', '<</Filter/FlateDecode /Length 2278>>', 'stream', '$z(R']\n",
        "results: ['%PDF-1.6', '1 0 obj', '<</MediaBox [0 0 1 1] /Type/Page /Contents 3 0 R /Parent 5 0 R>>', 'endobj', '5 0 obj']\n",
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     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: right; margin: 50px 0px 0px 30px\"><img src=\"files/images/eyeball_2.jpg\" width=\"300px\"></div>\n",
      "# Views\n",
      "Views exemplify the true power of the workbench. They are meta workers in the broadest sense, they can call any set of workers (and other views, which are just workers of course). All of the previous notebook code focused on demonstrating the level of control and granularity you can use with workbench, here the example we're going to show for views will be for those who don't care about granularity and really just want a big 'GO' button.\n",
      "\n",
      "Views can also be precise or general (example shows the latter):\n",
      "\n",
      "    - Customer billing View\n",
      "    - Sample volume over time View\n",
      "    - All samples that use communications calls View\n",
      "    - DO_EVERYTHING_BECAUSE_I_WANT_TO_PUNCH_GRANULARITY_IN_THE_NUTS! View\n",
      "    \n",
      "So lets look at the last kind .. it's called 'view' and like many of the other workers it's 20 lines of code.\n",
      "\n",
      "But it's deceptively simple, if you think about what must be happening below... over a dozen workers are getting orchestrated and run only when it makes sense for that MIME type. So with a few  'pull' calls the recursive dependency chains are invoked; work is done if/when it's needed and the whole thing is fantastically elegant and efficient. If your mind isn't a little bit blown by what happens below then you might not be paying attention."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This just grabs all the file_paths recursively\n",
      "def tag_type(path):\n",
      "    types = ['bro','json','log','pcap','pdf','exe','swf','zip']\n",
      "    for try_type in types:\n",
      "        if try_type in os.path.dirname(path):\n",
      "            return try_type\n",
      "\n",
      "file_list = []\n",
      "for p,d,f_list in os.walk('../data'):\n",
      "    file_list += [os.path.join(p, f) for f in f_list]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: left; margin: 0px 0px 0px 50px\"><img src=\"files/images/anxious.jpg\" width=\"150px\"></div>\n",
      "### Please note the next cell takes several minutes to complete\n",
      "#### The repository has hundreds of test files and the next few lines of python runs them all through 4-5 workers each, yes 'view' is like a magic pony it will figure out what workers to run on which file types (peid is the big time hog in case you're wondering)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# We're going to load in all the files which include PE files, PCAPS, PDFs, and ZIPs and run 'view' on them.\n",
      "# Note: This takes a while :)\n",
      "import pprint\n",
      "results = []\n",
      "for filename in file_list:\n",
      "    with open(filename,'rb') as f:\n",
      "        md5 = c.store_sample(f.read(), os.path.basename(filename), tag_type(filename))\n",
      "        results.append(c.work_request('view', md5))\n",
      "pprint.pprint(results[:5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[{'view': {'md5': '142372845adfdb668ba5bca0e81e6c19',\n",
        "           'meta': {'customer': 'Mega Corp',\n",
        "                    'encoding': 'binary',\n",
        "                    'file_size': 12292,\n",
        "                    'file_type': 'Apple Desktop Services Store',\n",
        "                    'filename': '.DS_Store',\n",
        "                    'import_time': '2014-06-10T20:48:33.501000Z',\n",
        "                    'length': 12292,\n",
        "                    'md5': '142372845adfdb668ba5bca0e81e6c19',\n",
        "                    'mime_type': 'binary',\n",
        "                    'type_tag': None}}},\n",
        " {'view': {'md5': 'f12f0237be84a8e353477e55ec43589b',\n",
        "           'meta': {'customer': 'Dorseys Mom',\n",
        "                    'encoding': 'us-ascii',\n",
        "                    'file_size': 25218,\n",
        "                    'file_type': 'ASCII text',\n",
        "                    'filename': 'conn.log',\n",
        "                    'import_time': '2014-06-10T20:48:33.514000Z',\n",
        "                    'length': 25218,\n",
        "                    'md5': 'f12f0237be84a8e353477e55ec43589b',\n",
        "                    'mime_type': 'text/plain',\n",
        "                    'type_tag': 'bro'}}},\n",
        " {'view': {'md5': 'a62fd3c72c1d688ff8041e0be87d07aa',\n",
        "           'meta': {'customer': 'Dorseys Mom',\n",
        "                    'encoding': 'us-ascii',\n",
        "                    'file_size': 541,\n",
        "                    'file_type': 'ASCII text',\n",
        "                    'filename': 'dhcp.log',\n",
        "                    'import_time': '2014-06-10T20:48:33.532000Z',\n",
        "                    'length': 541,\n",
        "                    'md5': 'a62fd3c72c1d688ff8041e0be87d07aa',\n",
        "                    'mime_type': 'text/plain',\n",
        "                    'type_tag': 'bro'}}},\n",
        " {'view': {'md5': '438022b94b10cada18414314b0c8584b',\n",
        "           'meta': {'customer': 'Huge Inc',\n",
        "                    'encoding': 'us-ascii',\n",
        "                    'file_size': 23896,\n",
        "                    'file_type': 'ASCII text',\n",
        "                    'filename': 'dns.log',\n",
        "                    'import_time': '2014-06-10T20:48:33.551000Z',\n",
        "                    'length': 23896,\n",
        "                    'md5': '438022b94b10cada18414314b0c8584b',\n",
        "                    'mime_type': 'text/plain',\n",
        "                    'type_tag': 'bro'}}},\n",
        " {'view': {'md5': 'f57da114f0f1e07b93f374d68c65c583',\n",
        "           'meta': {'customer': 'Dorseys Mom',\n",
        "                    'encoding': 'us-ascii',\n",
        "                    'file_size': 40283,\n",
        "                    'file_type': 'ASCII text',\n",
        "                    'filename': 'files.log',\n",
        "                    'import_time': '2014-06-10T20:48:33.573000Z',\n",
        "                    'length': 40283,\n",
        "                    'md5': 'f57da114f0f1e07b93f374d68c65c583',\n",
        "                    'mime_type': 'text/plain',\n",
        "                    'type_tag': 'bro'}}}]\n"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Okay so views can either aggregate results from multiple workers or they\n",
      "# can subset to just want you want (webpage presentation for instance)\n",
      "results = c.batch_work_request('view_customer')\n",
      "print results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<generator object iterator at 0x10e3375a0>\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div style=\"float: left; margin: 0px 0px 0px 30px\"><img src=\"files/images/head_explode.jpg\" width=\"350px\"></div>\n",
      "## Holy s#@&! The server batch request returned a generator?\n",
      "#### Yes generators are awesome but getting one from a server request! Are u serious?!  Yes, thanks to ZeroRPC...dead serious.. like chopping off your head and kicking your body into a shallow grave and putting your head on a stick... serious.\n",
      "\n",
      "#### For more on client/server generators and client-contructed/server-executed generator pipelines see our super spiffy [Generator Pipelines](http://nbviewer.ipython.org/url/raw.github.com/SuperCowPowers/workbench/master/workbench/notebooks/Generator_Pipelines.ipynb) notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# At this granularity it opens up a new world\n",
      "import pandas as pd\n",
      "df = pd.DataFrame(results)\n",
      "df.head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>customer</th>\n",
        "      <th>filename</th>\n",
        "      <th>import_time</th>\n",
        "      <th>length</th>\n",
        "      <th>md5</th>\n",
        "      <th>type_tag</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    BearTron</td>\n",
        "      <td> ../data/pe/bad/0cb9aa6fb9c4aa3afad7a303e21ac0f3</td>\n",
        "      <td> 2014-06-10T20:48:15.321000Z</td>\n",
        "      <td> 20480</td>\n",
        "      <td> 0cb9aa6fb9c4aa3afad7a303e21ac0f3</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>   Mega Corp</td>\n",
        "      <td> ../data/pe/bad/033d91aae8ad29ed9fbb858179271232</td>\n",
        "      <td> 2014-06-10T20:48:19.036000Z</td>\n",
        "      <td> 85504</td>\n",
        "      <td> 033d91aae8ad29ed9fbb858179271232</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Dorseys Mom</td>\n",
        "      <td> ../data/pe/bad/0e882ec9b485979ea84c7843d41ba36f</td>\n",
        "      <td> 2014-06-10T20:48:19.146000Z</td>\n",
        "      <td> 64512</td>\n",
        "      <td> 0e882ec9b485979ea84c7843d41ba36f</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>   Mega Corp</td>\n",
        "      <td> ../data/pe/bad/0e8b030fb6ae48ffd29e520fc16b5641</td>\n",
        "      <td> 2014-06-10T20:48:19.258000Z</td>\n",
        "      <td> 81920</td>\n",
        "      <td> 0e8b030fb6ae48ffd29e520fc16b5641</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Dorseys Mom</td>\n",
        "      <td> ../data/pe/bad/0eb9e990c521b30428a379700ec5ab3e</td>\n",
        "      <td> 2014-06-10T20:48:19.519000Z</td>\n",
        "      <td> 97280</td>\n",
        "      <td> 0eb9e990c521b30428a379700ec5ab3e</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> Dorseys Mom</td>\n",
        "      <td> ../data/pe/bad/127f2bade752445b3dbf2cf2ea75c201</td>\n",
        "      <td> 2014-06-10T20:48:19.697000Z</td>\n",
        "      <td> 66560</td>\n",
        "      <td> 127f2bade752445b3dbf2cf2ea75c201</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>    Huge Inc</td>\n",
        "      <td> ../data/pe/bad/139385a91b9bca0833bdc1fa77e42b91</td>\n",
        "      <td> 2014-06-10T20:48:19.817000Z</td>\n",
        "      <td> 22510</td>\n",
        "      <td> 139385a91b9bca0833bdc1fa77e42b91</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>   Mega Corp</td>\n",
        "      <td> ../data/pe/bad/13dcc5b4570180118eb65529b77f6d89</td>\n",
        "      <td> 2014-06-10T20:48:19.897000Z</td>\n",
        "      <td> 29184</td>\n",
        "      <td> 13dcc5b4570180118eb65529b77f6d89</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>    BearTron</td>\n",
        "      <td> ../data/pe/bad/1cac80a2147cd8f3860547e43edcaa00</td>\n",
        "      <td> 2014-06-10T20:48:20.009000Z</td>\n",
        "      <td> 72704</td>\n",
        "      <td> 1cac80a2147cd8f3860547e43edcaa00</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>   Mega Corp</td>\n",
        "      <td> ../data/pe/bad/1cea13cf888cd8ce4f869029f1dbb601</td>\n",
        "      <td> 2014-06-10T20:48:20.160000Z</td>\n",
        "      <td> 53248</td>\n",
        "      <td> 1cea13cf888cd8ce4f869029f1dbb601</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "      customer                                         filename  \\\n",
        "0     BearTron  ../data/pe/bad/0cb9aa6fb9c4aa3afad7a303e21ac0f3   \n",
        "1    Mega Corp  ../data/pe/bad/033d91aae8ad29ed9fbb858179271232   \n",
        "2  Dorseys Mom  ../data/pe/bad/0e882ec9b485979ea84c7843d41ba36f   \n",
        "3    Mega Corp  ../data/pe/bad/0e8b030fb6ae48ffd29e520fc16b5641   \n",
        "4  Dorseys Mom  ../data/pe/bad/0eb9e990c521b30428a379700ec5ab3e   \n",
        "5  Dorseys Mom  ../data/pe/bad/127f2bade752445b3dbf2cf2ea75c201   \n",
        "6     Huge Inc  ../data/pe/bad/139385a91b9bca0833bdc1fa77e42b91   \n",
        "7    Mega Corp  ../data/pe/bad/13dcc5b4570180118eb65529b77f6d89   \n",
        "8     BearTron  ../data/pe/bad/1cac80a2147cd8f3860547e43edcaa00   \n",
        "9    Mega Corp  ../data/pe/bad/1cea13cf888cd8ce4f869029f1dbb601   \n",
        "\n",
        "                   import_time  length                               md5  \\\n",
        "0  2014-06-10T20:48:15.321000Z   20480  0cb9aa6fb9c4aa3afad7a303e21ac0f3   \n",
        "1  2014-06-10T20:48:19.036000Z   85504  033d91aae8ad29ed9fbb858179271232   \n",
        "2  2014-06-10T20:48:19.146000Z   64512  0e882ec9b485979ea84c7843d41ba36f   \n",
        "3  2014-06-10T20:48:19.258000Z   81920  0e8b030fb6ae48ffd29e520fc16b5641   \n",
        "4  2014-06-10T20:48:19.519000Z   97280  0eb9e990c521b30428a379700ec5ab3e   \n",
        "5  2014-06-10T20:48:19.697000Z   66560  127f2bade752445b3dbf2cf2ea75c201   \n",
        "6  2014-06-10T20:48:19.817000Z   22510  139385a91b9bca0833bdc1fa77e42b91   \n",
        "7  2014-06-10T20:48:19.897000Z   29184  13dcc5b4570180118eb65529b77f6d89   \n",
        "8  2014-06-10T20:48:20.009000Z   72704  1cac80a2147cd8f3860547e43edcaa00   \n",
        "9  2014-06-10T20:48:20.160000Z   53248  1cea13cf888cd8ce4f869029f1dbb601   \n",
        "\n",
        "  type_tag  \n",
        "0       pe  \n",
        "1       pe  \n",
        "2       pe  \n",
        "3       pe  \n",
        "4       pe  \n",
        "5       pe  \n",
        "6       pe  \n",
        "7       pe  \n",
        "8       pe  \n",
        "9       pe  \n",
        "\n",
        "[10 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Lets look at the file submission types broken down by customer\n",
      "df['count'] = 1\n",
      "df.groupby(['customer','type_tag']).sum()"
     ],
     "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></th>\n",
        "      <th>length</th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>customer</th>\n",
        "      <th>type_tag</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"7\" valign=\"top\">BearTron</th>\n",
        "      <th>bro</th>\n",
        "      <td>   52422</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cab</th>\n",
        "      <td>   54007</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>json</th>\n",
        "      <td>  288312</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>own</th>\n",
        "      <td>     554</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pcap</th>\n",
        "      <td>   35057</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pdf</th>\n",
        "      <td>  374438</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pe</th>\n",
        "      <td> 1588841</td>\n",
        "      <td> 28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">Dorseys Mom</th>\n",
        "      <th>bro</th>\n",
        "      <td>  146979</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>jar</th>\n",
        "      <td>   10629</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pcap</th>\n",
        "      <td> 1799435</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pdf</th>\n",
        "      <td>  251385</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pe</th>\n",
        "      <td> 1673734</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>zip</th>\n",
        "      <td>  151268</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">Huge Inc</th>\n",
        "      <th>bro</th>\n",
        "      <td>  197288</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>own</th>\n",
        "      <td>   52961</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pcap</th>\n",
        "      <td> 3461100</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pdf</th>\n",
        "      <td>  512620</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pe</th>\n",
        "      <td> 1120037</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>swf</th>\n",
        "      <td>    7724</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">Mega Corp</th>\n",
        "      <th>bro</th>\n",
        "      <td>   59415</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>jar</th>\n",
        "      <td>   18643</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>own</th>\n",
        "      <td>   10252</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pcap</th>\n",
        "      <td>  693558</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pdf</th>\n",
        "      <td>  390356</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pe</th>\n",
        "      <td> 1507236</td>\n",
        "      <td> 27</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>25 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "                       length  count\n",
        "customer    type_tag                \n",
        "BearTron    bro         52422     12\n",
        "            cab         54007      1\n",
        "            json       288312      1\n",
        "            own           554      1\n",
        "            pcap        35057      3\n",
        "            pdf        374438     13\n",
        "            pe        1588841     28\n",
        "Dorseys Mom bro        146979     20\n",
        "            jar         10629      1\n",
        "            pcap      1799435      1\n",
        "            pdf        251385      9\n",
        "            pe        1673734     27\n",
        "            zip        151268      2\n",
        "Huge Inc    bro        197288      8\n",
        "            own         52961      3\n",
        "            pcap      3461100      3\n",
        "            pdf        512620     18\n",
        "            pe        1120037     23\n",
        "            swf          7724      1\n",
        "Mega Corp   bro         59415     12\n",
        "            jar         18643      1\n",
        "            own         10252      4\n",
        "            pcap       693558      1\n",
        "            pdf        390356     12\n",
        "            pe        1507236     27\n",
        "\n",
        "[25 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plotting defaults\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "plt.rcParams['font.size'] = 12.0\n",
      "plt.rcParams['figure.figsize'] = 18.0, 8.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot box plots based on customer (PDFs)\n",
      "df[df['type_tag']=='pdf'].boxplot('length','customer')\n",
      "plt.xlabel('Customer')\n",
      "plt.ylabel('File Size')\n",
      "plt.title('File Length (PDF) by Customer')\n",
      "plt.suptitle('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<matplotlib.text.Text at 0x10e5addd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x10e598150>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot box plots based on customer (PEs)\n",
      "df[df['type_tag']=='exe'].boxplot('length','customer')\n",
      "plt.xlabel('Customer')\n",
      "plt.ylabel('File Size')\n",
      "plt.title('File Length (PE) by Customer')\n",
      "plt.suptitle('')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "<matplotlib.text.Text at 0x10e661cd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YXB0CADfAeQHqQ9IFAAAAAACgCVDTxQnUdAEA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\nJemSSy7Rxo0bNXPmzHP2DRw4UPn5+Vq2bJlOnjyp5cuXa8e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       "text": [
        "<matplotlib.figure.Figure at 0x10e297450>"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Okay now lets do some plots on the file meta-data\n",
      "results = c.batch_work_request('meta_deep')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_meta = pd.DataFrame(results)\n",
      "df_meta.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>customer</th>\n",
        "      <th>encoding</th>\n",
        "      <th>entropy</th>\n",
        "      <th>file_size</th>\n",
        "      <th>file_type</th>\n",
        "      <th>filename</th>\n",
        "      <th>import_time</th>\n",
        "      <th>length</th>\n",
        "      <th>md5</th>\n",
        "      <th>mime_type</th>\n",
        "      <th>sha1</th>\n",
        "      <th>sha256</th>\n",
        "      <th>ssdeep</th>\n",
        "      <th>type_tag</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>    BearTron</td>\n",
        "      <td> binary</td>\n",
        "      <td> 2.440069</td>\n",
        "      <td> 20480</td>\n",
        "      <td> PE32 executable (GUI) Intel 80386, for MS Windows</td>\n",
        "      <td> ../data/pe/bad/0cb9aa6fb9c4aa3afad7a303e21ac0f3</td>\n",
        "      <td> 2014-06-10T20:48:15.321000Z</td>\n",
        "      <td> 20480</td>\n",
        "      <td> 0cb9aa6fb9c4aa3afad7a303e21ac0f3</td>\n",
        "      <td> application/x-dosexec</td>\n",
        "      <td> 96e85768a12b2f319f2a4f0c048460e1b73aa573</td>\n",
        "      <td> 4ecf79302ba0439f62e15d0526a297975e6bb32ea25c8c...</td>\n",
        "      <td> 192:a8jJIFYrq9ATskBTp2jLDL3P1oynldvSo71nF:oFpN...</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>   Mega Corp</td>\n",
        "      <td> binary</td>\n",
        "      <td> 7.894680</td>\n",
        "      <td> 85504</td>\n",
        "      <td> PE32 executable (GUI) Intel 80386, for MS Windows</td>\n",
        "      <td> ../data/pe/bad/033d91aae8ad29ed9fbb858179271232</td>\n",
        "      <td> 2014-06-10T20:48:19.036000Z</td>\n",
        "      <td> 85504</td>\n",
        "      <td> 033d91aae8ad29ed9fbb858179271232</td>\n",
        "      <td> application/x-dosexec</td>\n",
        "      <td> 83ab10907b254752f312c89125957f10d35cb9d4</td>\n",
        "      <td> eb107c004e6e1bbd3b32ad7961661bbe28a577b0cb5dac...</td>\n",
        "      <td> 1536:h6+LbfPbI5dzmJu9Tgj5aOItvEqRCHW9pjVrs2ryr...</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Dorseys Mom</td>\n",
        "      <td> binary</td>\n",
        "      <td> 5.125292</td>\n",
        "      <td> 64512</td>\n",
        "      <td> PE32 executable (GUI) Intel 80386, for MS Windows</td>\n",
        "      <td> ../data/pe/bad/0e882ec9b485979ea84c7843d41ba36f</td>\n",
        "      <td> 2014-06-10T20:48:19.146000Z</td>\n",
        "      <td> 64512</td>\n",
        "      <td> 0e882ec9b485979ea84c7843d41ba36f</td>\n",
        "      <td> application/x-dosexec</td>\n",
        "      <td> 12fb0a1b7d9c2b2a41f4da9ce5bbfb140fb16939</td>\n",
        "      <td> 616cf9e729c883d979212eb55178b7aac80dd9f58cb449...</td>\n",
        "      <td> 768:5HyLMqtEM1Htz8kDmP9l+nZZYp41oj7EZmJxl/N9j6...</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>   Mega Corp</td>\n",
        "      <td> binary</td>\n",
        "      <td> 6.303055</td>\n",
        "      <td> 81920</td>\n",
        "      <td> PE32 executable (GUI) Intel 80386, for MS Windows</td>\n",
        "      <td> ../data/pe/bad/0e8b030fb6ae48ffd29e520fc16b5641</td>\n",
        "      <td> 2014-06-10T20:48:19.258000Z</td>\n",
        "      <td> 81920</td>\n",
        "      <td> 0e8b030fb6ae48ffd29e520fc16b5641</td>\n",
        "      <td> application/x-dosexec</td>\n",
        "      <td> 82d57b8302b7497b2f6943f18e2d2687b9b0f5eb</td>\n",
        "      <td> feaf72bdad035e198d297bfb0b8d891645f1dacd78f0db...</td>\n",
        "      <td> 1536:1uNqjqzs1hQHhInEeJMzcmGqyF7Jwe9pvUo+5TDU4...</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Dorseys Mom</td>\n",
        "      <td> binary</td>\n",
        "      <td> 7.593283</td>\n",
        "      <td> 97280</td>\n",
        "      <td> PE32 executable (GUI) Intel 80386, for MS Windows</td>\n",
        "      <td> ../data/pe/bad/0eb9e990c521b30428a379700ec5ab3e</td>\n",
        "      <td> 2014-06-10T20:48:19.519000Z</td>\n",
        "      <td> 97280</td>\n",
        "      <td> 0eb9e990c521b30428a379700ec5ab3e</td>\n",
        "      <td> application/x-dosexec</td>\n",
        "      <td> b778fc55f0538de865d4853099a3faa0b29f311d</td>\n",
        "      <td> dc5e8176a5f012ebdb4835f9b570a12c045d059f6f5bdc...</td>\n",
        "      <td> 1536:KcE4iMgXjTJpdGaaJG6Mhawv7r9ZaobsLBq+h5ttB...</td>\n",
        "      <td> pe</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 14 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "      customer encoding   entropy  file_size  \\\n",
        "0     BearTron   binary  2.440069      20480   \n",
        "1    Mega Corp   binary  7.894680      85504   \n",
        "2  Dorseys Mom   binary  5.125292      64512   \n",
        "3    Mega Corp   binary  6.303055      81920   \n",
        "4  Dorseys Mom   binary  7.593283      97280   \n",
        "\n",
        "                                           file_type  \\\n",
        "0  PE32 executable (GUI) Intel 80386, for MS Windows   \n",
        "1  PE32 executable (GUI) Intel 80386, for MS Windows   \n",
        "2  PE32 executable (GUI) Intel 80386, for MS Windows   \n",
        "3  PE32 executable (GUI) Intel 80386, for MS Windows   \n",
        "4  PE32 executable (GUI) Intel 80386, for MS Windows   \n",
        "\n",
        "                                          filename  \\\n",
        "0  ../data/pe/bad/0cb9aa6fb9c4aa3afad7a303e21ac0f3   \n",
        "1  ../data/pe/bad/033d91aae8ad29ed9fbb858179271232   \n",
        "2  ../data/pe/bad/0e882ec9b485979ea84c7843d41ba36f   \n",
        "3  ../data/pe/bad/0e8b030fb6ae48ffd29e520fc16b5641   \n",
        "4  ../data/pe/bad/0eb9e990c521b30428a379700ec5ab3e   \n",
        "\n",
        "                   import_time  length                               md5  \\\n",
        "0  2014-06-10T20:48:15.321000Z   20480  0cb9aa6fb9c4aa3afad7a303e21ac0f3   \n",
        "1  2014-06-10T20:48:19.036000Z   85504  033d91aae8ad29ed9fbb858179271232   \n",
        "2  2014-06-10T20:48:19.146000Z   64512  0e882ec9b485979ea84c7843d41ba36f   \n",
        "3  2014-06-10T20:48:19.258000Z   81920  0e8b030fb6ae48ffd29e520fc16b5641   \n",
        "4  2014-06-10T20:48:19.519000Z   97280  0eb9e990c521b30428a379700ec5ab3e   \n",
        "\n",
        "               mime_type                                      sha1  \\\n",
        "0  application/x-dosexec  96e85768a12b2f319f2a4f0c048460e1b73aa573   \n",
        "1  application/x-dosexec  83ab10907b254752f312c89125957f10d35cb9d4   \n",
        "2  application/x-dosexec  12fb0a1b7d9c2b2a41f4da9ce5bbfb140fb16939   \n",
        "3  application/x-dosexec  82d57b8302b7497b2f6943f18e2d2687b9b0f5eb   \n",
        "4  application/x-dosexec  b778fc55f0538de865d4853099a3faa0b29f311d   \n",
        "\n",
        "                                              sha256  \\\n",
        "0  4ecf79302ba0439f62e15d0526a297975e6bb32ea25c8c...   \n",
        "1  eb107c004e6e1bbd3b32ad7961661bbe28a577b0cb5dac...   \n",
        "2  616cf9e729c883d979212eb55178b7aac80dd9f58cb449...   \n",
        "3  feaf72bdad035e198d297bfb0b8d891645f1dacd78f0db...   \n",
        "4  dc5e8176a5f012ebdb4835f9b570a12c045d059f6f5bdc...   \n",
        "\n",
        "                                              ssdeep type_tag  \n",
        "0  192:a8jJIFYrq9ATskBTp2jLDL3P1oynldvSo71nF:oFpN...       pe  \n",
        "1  1536:h6+LbfPbI5dzmJu9Tgj5aOItvEqRCHW9pjVrs2ryr...       pe  \n",
        "2  768:5HyLMqtEM1Htz8kDmP9l+nZZYp41oj7EZmJxl/N9j6...       pe  \n",
        "3  1536:1uNqjqzs1hQHhInEeJMzcmGqyF7Jwe9pvUo+5TDU4...       pe  \n",
        "4  1536:KcE4iMgXjTJpdGaaJG6Mhawv7r9ZaobsLBq+h5ttB...       pe  \n",
        "\n",
        "[5 rows x 14 columns]"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot entropy box plots based on file type\n",
      "df_meta.boxplot('entropy','type_tag')\n",
      "plt.xlabel('Mime Type')\n",
      "plt.ylabel('Entropy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "<matplotlib.text.Text at 0x10f436410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bT27Wk1eZ1hJhPUCxzNwAAAAASktzo4WqtL+pE8knN/nkJZvc5JObfPKSTW7y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\nEREREdGA9l8kG47+AjET6QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f42f510>"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot customer submissions based on file type\n",
      "group_df = df[['customer','type_tag']]\n",
      "group_df['submissions'] = 1\n",
      "group_df = group_df.groupby(['customer','type_tag']).sum().unstack()\n",
      "group_df.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>\n",
        "      <th></th>\n",
        "      <th colspan=\"10\" halign=\"left\">submissions</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>type_tag</th>\n",
        "      <th>bro</th>\n",
        "      <th>cab</th>\n",
        "      <th>jar</th>\n",
        "      <th>json</th>\n",
        "      <th>own</th>\n",
        "      <th>pcap</th>\n",
        "      <th>pdf</th>\n",
        "      <th>pe</th>\n",
        "      <th>swf</th>\n",
        "      <th>zip</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>customer</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>BearTron</th>\n",
        "      <td> 12</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 13</td>\n",
        "      <td> 28</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dorseys Mom</th>\n",
        "      <td> 20</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1</td>\n",
        "      <td>  9</td>\n",
        "      <td> 27</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Huge Inc</th>\n",
        "      <td>  8</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 18</td>\n",
        "      <td> 23</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mega Corp</th>\n",
        "      <td> 12</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 12</td>\n",
        "      <td> 27</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "             submissions                                              \n",
        "type_tag             bro  cab  jar  json  own  pcap  pdf  pe  swf  zip\n",
        "customer                                                              \n",
        "BearTron              12    1  NaN     1    1     3   13  28  NaN  NaN\n",
        "Dorseys Mom           20  NaN    1   NaN  NaN     1    9  27  NaN    2\n",
        "Huge Inc               8  NaN  NaN   NaN    3     3   18  23    1  NaN\n",
        "Mega Corp             12  NaN    1   NaN    4     1   12  27  NaN  NaN\n",
        "\n",
        "[4 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot entropy box plots based on mime-type\n",
      "my_colors = [(x/9.0, .8, 1.0-x/9.0) for x in range(10)] # Why the heck dosen't matplotlib have better categorical cmaps?\n",
      "group_df['submissions'].plot(kind='bar', stacked=True, color=my_colors)\n",
      "plt.xlabel('Customer')\n",
      "plt.ylabel('Submissions')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "<matplotlib.text.Text at 0x10f9f6e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Tpk2VnJysDRs2aMOGDUpISFDt2rUrPOe/lyWpvLzcXyWfFebZALOQWcAc5BUw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\nvMLJvBq1+PLLL31dB7wQFRWlEydO2F0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       "text": [
        "<matplotlib.figure.Figure at 0x10f78a6d0>"
       ]
      }
     ],
     "prompt_number": 80
    }
   ],
   "metadata": {}
  }
 ]
}