Analysing network traffic with Pandas

gistfile1.txt

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  "name": "pcap"
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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Analysing network traffic with Pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dirk Loss, http://dirk-loss.de, @dloss. \n",
      "v1.1, 2013-06-02"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This IPython notebook shows how to analyse network traffic using the following tools:\n",
      "    \n",
      "* **Pandas**, a Python library for analysing data <http://pandas.pydata.org/>\n",
      "* **tshark**, the command line version of the Wireshark sniffer <http://http://www.wireshark.org/>\n",
      "* **Matplotlib**, a Python plotting library <http://matplotlib.org/>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas allows for very flexible analysis, treating your PCAP files as a timeseries of packet data. \n",
      "\n",
      "So if the statistics provided by Wireshark are not enough, you might want to try this. And it's more fun, of course. :) "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Get a PCAP file"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First we need a PCAP file. I chose a sample file from the Digital Corpora site that has been used for courses in network forensics:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "HTML('<iframe src=http://digitalcorpora.org/corpora/scenarios/nitroba-university-harassment-scenario width=600 height=300></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://digitalcorpora.org/corpora/scenarios/nitroba-university-harassment-scenario width=600 height=300></iframe>"
       ],
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x9492eac>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!mkdir -p pcap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd pcap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/dirk/projects/pcap"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can download it using curl or pure Python. Just uncomment one of the following cells:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url=\"http://digitalcorpora.org/corp/nps/packets/2008-nitroba/nitroba.pcap\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# If you have curl installed, we can get nice progress bars:\n",
      "#!curl -o nitroba.pcap $url"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Or use pure Python:\n",
      "# import urllib\n",
      "# urllib.urlretrieve(url, \"nitroba.pcap\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls -l nitroba.pcap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-rw-r-- 1 dirk dirk 56795590 Jun  2 12:10 nitroba.pcap\r\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!md5sum nitroba.pcap"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d6b5df10fc572b54ceb9c543d11f10a4  nitroba.pcap\r\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Convert PCAP to a CSV using tshark"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can use the `tshark` command from the Wireshark tool suite to read the PCAP file and convert it into a tab-separated file. This might not be very fast, but it is very flexible, because all of Wireshark's diplay filters can be used to select the packets that we are interested in."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!tshark -v"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "TShark 1.6.7\r\n",
        "\r\n",
        "Copyright 1998-2012 Gerald Combs <gerald@wireshark.org> and contributors.\r\n",
        "This is free software; see the source for copying conditions. There is NO\r\n",
        "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\r\n",
        "\r\n",
        "Compiled (32-bit) with GLib 2.32.0, with libpcap (version unknown), with libz\r\n",
        "1.2.3.4, with POSIX capabilities (Linux), without libpcre, with SMI 0.4.8, with\r\n",
        "c-ares 1.7.5, with Lua 5.1, without Python, with GnuTLS 2.12.14, with Gcrypt\r\n",
        "1.5.0, with MIT Kerberos, with GeoIP.\r\n",
        "\r\n",
        "Running on Linux 3.2.0-45-generic, with libpcap version 1.1.1, with libz\r\n",
        "1.2.3.4.\r\n",
        "\r\n",
        "Built using gcc 4.6.3.\r\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For now, I just select the frame number and the frame length and redirect the output to a file:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!tshark -n -r nitroba.pcap -T fields -Eheader=y -e frame.number -e frame.len > frame.len"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's have a look at the file:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -10 frame.len"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "frame.number\tframe.len\r\n",
        "1\t70\r\n",
        "2\t70\r\n",
        "3\t1421\r\n",
        "4\t70\r\n",
        "5\t1284\r\n",
        "6\t70\r\n",
        "7\t70\r\n",
        "8\t70\r\n",
        "9\t78\r\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Two columns, tab-separaed. (Not exactly CSV, but who cares. ;-)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas can read those tables into a DataFrame object:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df=pd.read_table(\"frame.len\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The object has a nice default representation that shows the number of values in each row:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "Int64Index: 95175 entries, 0 to 95174\n",
        "Data columns (total 2 columns):\n",
        "frame.number    95175  non-null values\n",
        "frame.len       95175  non-null values\n",
        "dtypes: int64(2)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 95175 entries, 0 to 95174\n",
        "Data columns (total 2 columns):\n",
        "frame.number    95175  non-null values\n",
        "frame.len       95175  non-null values\n",
        "dtypes: int64(2)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some statistics about the frame length:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[\"frame.len\"].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "count    95175.000000\n",
        "mean       580.748789\n",
        "std        625.757017\n",
        "min         42.000000\n",
        "25%         70.000000\n",
        "50%         87.000000\n",
        "75%       1466.000000\n",
        "max       1466.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The minimum and maximum frame lengths are plausible for an Ethernet connection."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For a better overview, we plot the frame length over time."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We initialise IPython to show inline graphics:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set a figure size in inches:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(10,6)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas automatically uses Matplotlib for plotting. We plot with small dots and an alpha channel of 0.2:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "df[\"frame.len\"].plot(style=\".\", alpha=0.2)\n",
      "title(\"Frame length\")\n",
      "ylabel(\"bytes\")\n",
      "xlabel(\"frame number\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.text.Text at 0x9aaf58c>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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DhwPYvNn8/cePB3D4MLB4MfvXSf3FWB49Cowfz/cNHhxAUpLx/UVFfdu/Hg9w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\n0Usv2TvQCYY1NZVM6r59vC76pL5eOyQb2eBFY5M3Y9adhpiqrWXauc2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49Py2Ogll3CyGnGjOAWj7XE5yPauRP7TAyQHcs6kducTE3/SFPaYPIWL4ZH0MAAGMexYwApi4mQ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\n4z53ztdpVg0xQmnf+hbeczhAoD/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bsZ0/D+Du6gLycTb1q13X5CT8u4jAhP74R4FsBQUY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\nWonZWZyXrN3Q6XAZ4TqWcouIUA9sMRjEWYayDvbz4zxhPT1gvN3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T1sXBL3V16ucQLOG5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aHIyEgqLi4mIqKioiIaNGgQERHFxMRQfn5++70FBQUUExPT4Zk+PjBzhIdDTc8+JyaTrAzQ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\nT4cOHTp06NChox/i/wPz/tleifpt+wAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So there are always lots of small packets (< 100 bytes) and lots of large packets (> 1400 bytes). Some bursts of packets with other sizes (around 400 bytes, 1000 bytes, etc.) can be clearly seen."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "A Python function to read PCAP files into Pandas DataFrames"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Passing all those arguments to tshark is quite cumbersome. Here is a convenience function that reads the given fields into a Pandas DataFrame:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import subprocess\n",
      "import datetime\n",
      "import pandas as pd\n",
      "\n",
      "def read_pcap(filename, fields=[], display_filter=\"\", \n",
      "              timeseries=False, strict=False):\n",
      "    \"\"\" Read PCAP file into Pandas DataFrame object. \n",
      "    Uses tshark command-line tool from Wireshark.\n",
      "\n",
      "    filename:       Name or full path of the PCAP file to read\n",
      "    fields:         List of fields to include as columns\n",
      "    display_filter: Additional filter to restrict frames\n",
      "    strict:         Only include frames that contain all given fields \n",
      "                    (Default: false)\n",
      "    timeseries:     Create DatetimeIndex from frame.time_epoch \n",
      "                    (Default: false)\n",
      "\n",
      "    Syntax for fields and display_filter is specified in\n",
      "    Wireshark's Display Filter Reference:\n",
      " \n",
      "      http://www.wireshark.org/docs/dfref/\n",
      "    \"\"\"\n",
      "    if timeseries:\n",
      "        fields = [\"frame.time_epoch\"] + fields\n",
      "    fieldspec = \" \".join(\"-e %s\" % f for f in fields)\n",
      "\n",
      "    display_filters = fields if strict else []\n",
      "    if display_filter:\n",
      "        display_filters.append(display_filter)\n",
      "    filterspec = \"-R '%s'\" % \" and \".join(f for f in display_filters)\n",
      "\n",
      "    options = \"-r %s -n -T fields -Eheader=y\" % filename\n",
      "    cmd = \"tshark %s %s %s\" % (options, filterspec, fieldspec)\n",
      "    proc = subprocess.Popen(cmd, shell = True, \n",
      "                                 stdout=subprocess.PIPE)\n",
      "    if timeseries:\n",
      "        df = pd.read_table(proc.stdout, \n",
      "                        index_col = \"frame.time_epoch\", \n",
      "                        parse_dates=True, \n",
      "                        date_parser=datetime.datetime.fromtimestamp)\n",
      "    else:\n",
      "        df = pd.read_table(proc.stdout)\n",
      "    return df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will use this function in my further analysis."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Bandwidth"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By summing up the frame lengths we can calculate the complete (Ethernet) bandwidth used.\n",
      "First use our convenience function to read the PCAP into a DataFrame:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "framelen=read_pcap(\"nitroba.pcap\", [\"frame.len\"], timeseries=True)\n",
      "framelen"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "DatetimeIndex: 95175 entries, 2008-07-22 03:51:07.095278 to 2008-07-22 08:13:47.046029\n",
        "Data columns (total 1 columns):\n",
        "frame.len    95175  non-null values\n",
        "dtypes: int64(1)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 95175 entries, 2008-07-22 03:51:07.095278 to 2008-07-22 08:13:47.046029\n",
        "Data columns (total 1 columns):\n",
        "frame.len    95175  non-null values\n",
        "dtypes: int64(1)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then we re-sample the timeseries into buckets of 1 second, summing over the lengths of all frames that were captured in that second:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes_per_second=framelen.resample(\"S\", how=\"sum\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here are the first 5 rows. We get NaN for those timestamps where no frames were captured:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes_per_second.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>frame.len</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>frame.time_epoch</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2008-07-22 03:51:07</th>\n",
        "      <td> 20729</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-07-22 03:51:08</th>\n",
        "      <td>  8426</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-07-22 03:51:09</th>\n",
        "      <td> 13565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-07-22 03:51:10</th>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008-07-22 03:51:11</th>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "                     frame.len\n",
        "frame.time_epoch              \n",
        "2008-07-22 03:51:07      20729\n",
        "2008-07-22 03:51:08       8426\n",
        "2008-07-22 03:51:09      13565\n",
        "2008-07-22 03:51:10        NaN\n",
        "2008-07-22 03:51:11        NaN"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes_per_second.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x9f40d2c>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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gNTUVBQUFAIDq6mps3boV1dXVKCsrw+LFiyEuXuBftGgRioqKYDKZYDKZUFZW\nBgAoKipCVFQUTCYTli5dimXLlgGQAr+1a9eisrISlZWVWLNmTZ/gj4iI/Eup92vdeCOQmxvoUgQv\nV/ebmhrgwAHvLPvsWWDTJs/z8de+P+jly8suuwwA0NnZie7ubkRGRmLnzp3Izs4GAGRnZ2P79u0A\ngB07diArKwthYWHQ6/WIi4tDRUUFGhoa0NbWhuTkZADA/PnzrWl65zVnzhzs27cPALB7926kp6dD\nq9VCq9UiLS3NGsgRERFRcHGnNWvjRmD+fO+XxVdCB5vhwoULuP7663H06FEsWrQI1157LRobGxEd\nHQ0AiI6ORmNjIwCgvr4eKSkp1rQ6nQ5msxlhYWHQ6XTW8TExMTCbzQAAs9mM2NhYqTChoYiIiEBT\nUxPq6+v7pLHk1V9OTg70ev3F9FoAEwFMBQAYjUYAwNSpHOYwhznMYU+HpdYC3y9PCODoUSOMRtfT\nW8oH9B1ubnYvPw57NgxMhUbjevqamr71BRjR0iLlZ2v+jo6e5fVfvr3lnT/fM729ve/yTp404uzZ\nnunHjg2eX+/pgJSfZdrx48fhFOGk1tZW8cMf/lDs379faLXaPtMiIyOFEEIsWbJEbN682To+NzdX\nlJaWioMHD4pp06ZZxx84cEDcfvvtQgghxo8fL8xms3Xa6NGjxZkzZ8T69evFE088YR3/+OOPi/Xr\n1/dZbv/inz4thNTI6OxaERGRszQa/3y/Ll0qxDPPuJdWpxPi5Mmec8Edd0if6eneLSM5BxAiIsL1\nNGvXDhw3bZr9/U+nsz3thRfspxk2TIgzZ6TpcXF9pz36qBBPPtkzXFDg3L5v2e/szTtY2BXiXOgG\nRERE4Mc//jGqqqoQHR2NU6dOAQAaGhowfPhwAFILWG1trTVNXV0ddDodYmJiUNfrLkvLeEuakydP\nAgC6urpw9uxZREVFDcirtra2T8sZERERkS/I8t2XZ86csd5c397ejj179iApKQkZGRkoLi4GID0h\nOXv2bABARkYGSkpK0NnZiZqaGphMJiQnJ2PEiBEIDw9HRUUFhBDYtGkTZs2aZU1jyau0tBSpqakA\ngPT0dJSXl6O1tRUtLS3Ys2cPpk+f7putQEREqqbUhxQocALxRKbDe8oaGhqQnZ2NCxcu4MKFC5g3\nbx5SU1ORlJSEzMxMFBUVQa/XY9u2bQCAhIQEZGZmIiEhAaGhoSgsLITm4loVFhYiJycH7e3tmDlz\nJmbMmAGARkzmAAAgAElEQVQAyM3Nxbx582AwGBAVFYWSkhIAwLBhw7By5UpMmTIFALBq1SpotVqf\nbQgiIiKSh2ANoh0GZYmJifjoo48GjB82bBj27t1rM82KFSuwYsWKAeMnTZqEzz77bMD4Sy+91BrU\n9bdgwQIsWLDAURGJiIgGJZd+qIJRsAZY7nD6njIl4EFHROQ7PLmSWsll31ZVUEZERESkVAzKiIhI\n9eTSEkLkCIMyIiIiIhlgUEZERESqpLR7zRmUERERkazYutystADLHQzKiIhI9YLhhC5X3rqfz518\nlHYvIYMyIiJSjf4nYQZjyjBlCvDUU4EuReAxKCMiIlWxFYgprcUk2Bw8COza1TMcrHXIoIyIiFTh\nzBnAbA50KchX/NnqKcsXkhMRESnFtGmBLgGpSSAufTMoIyIiVfjkk0CXgGyR6319crwcyqCMiIgU\n75FHAl0C8jVfBHdyCxhVGZTde2+gS0BERP70978HugRkTzB0ZeEtqgzKLrkk0CUgIiIib5Jbq5Yv\nqDIoIyIiImUZLOhyp/VMaYGcKoOyYG32JCIKVko7+QaTtjags9PzfILh3K7KoIyIiJQrGE6+weaV\nVwJdAmVgUEZERLLDli916e72PA9f7hNy2d8YlBERkeLJ5aRK7hushTSQdeyv1lsGZURERCQr3gqC\nlHYpnEEZEREpHlvKyJv47ksvUlpkTERERI75+9zOd18SERF5EVvQlGOwuvJlXcqlMYdBGRERqZ5c\nTrrkvmAIsFUZlPHgIyIiUi5bARh79FcYBmNERESkVKGBLgARESnD6tXAt98GuhS29W4RsdU6orQW\nk2AXrI0sqmopIyIi3wkLA0Jl+lO+uTnQJSDyHIMyIiJSvMGCsmBteVGTYGjtVGVQxoOPiMg3vPX9\nWlXlnXxIGb7+evB5giHoGgyDMiIicoq3TppHjwKTJ3snL1KG5csDXQJlUGVQRkRE8sUfzmQL9wsG\nZURE5GdDhgS6BBQs3A30+O5LL+J1aSIi+QoJwJmH5wVlkUOrGd99SURE5CGdbuA4OZzkiQbDoIyI\niPyKARL5i9JaSBmUERERkV9oNMAnn9if5k9y/HGgyqBMjhuaiIiIgPr6QJegh9xa0hwGZbW1tfjR\nj36Ea6+9FuPHj8cLL7wAAGhubkZaWhri4+ORnp6O1tZWa5r8/HwYDAaMHTsW5eXl1vFVVVVITEyE\nwWBAXl6edXxHRwfmzp0Lg8GAlJQUnDhxwjqtuLgY8fHxiI+Px8aNG51eKQZlREQEyO+kS4E7R3d2\nBma5rnAYlIWFheHZZ5/Fv//9b3zwwQd46aWXcPjwYRQUFCAtLQ1HjhxBamoqCgoKAADV1dXYunUr\nqqurUVZWhsWLF0Nc3PqLFi1CUVERTCYTTCYTysrKAABFRUWIioqCyWTC0qVLsWzZMgBS4Ld27VpU\nVlaisrISa9as6RP8ERERDebChUCXgLzFkwC7uhq49FLvlcXiwAFg/Xrv5ecwKBsxYgQmTpwIALj8\n8ssxbtw4mM1m7Ny5E9nZ2QCA7OxsbN++HQCwY8cOZGVlISwsDHq9HnFxcaioqEBDQwPa2tqQnJwM\nAJg/f741Te+85syZg3379gEAdu/ejfT0dGi1Wmi1WqSlpVkDOSIiUi9vtqS0tXkvL/KOQLSUffWV\nb/I1m737yrBQZ2c8fvw4Pv74Y/zwhz9EY2MjoqOjAQDR0dFobGwEANTX1yMlJcWaRqfTwWw2Iyws\nDLpezyjHxMTAbDYDAMxmM2JjY6XChIYiIiICTU1NqK+v75PGkld/OTk50Ov1F9NrAUwEMBUAYDQa\nAQBTp3KYwxzmMIc9HT52DLjiCs/zk07KRhiN9uc/etTx9P7DgPHip+3hlhbX8uOwd4Z714c0yvb8\ngBHSxTBp+J13jLj+euCjj3qmSy+dt52+o6Pv8vov/9ChgdO7unqG29v77h+1tUacO9cz/dgxx/nb\nX19p2vHjx+EU4YS2tjZx/fXXi9dee00IIYRWq+0zPTIyUgghxJIlS8TmzZut43Nzc0Vpaak4ePCg\nmDZtmnX8gQMHxO233y6EEGL8+PHCbDZbp40ePVqcOXNGrF+/XjzxxBPW8Y8//rhYv359n+X2L/7p\n00IAQtx7rzNrRURErli3Tojlyz3P59gx6bvanrw8IZ591rU8pfaXgX8/+pH02esURH7Uuy4sw//8\np+35pk7t+f+qq/ruI4AQM2bY3290OtvTNmyQxr/11sDpWq0Qzc3S+NGj+05bulSIZ57pGc7Pt53/\nq68Kcffd9td34Ho6DrtCBgvazp8/jzlz5mDevHmYPXs2AKl17NSpUwCAhoYGDB8+HIDUAlZbW2tN\nW1dXB51Oh5iYGNTV1Q0Yb0lz8uRJAEBXVxfOnj2LqKioAXnV1tb2aTkjIiLPRUYCNTWBLgUFEz6M\nZ5/DoEwIgdzcXCQkJOBXv/qVdXxGRgaKi4sBSE9IWoK1jIwMlJSUoLOzEzU1NTCZTEhOTsaIESMQ\nHh6OiooKCCGwadMmzJo1a0BepaWlSE1NBQCkp6ejvLwcra2taGlpwZ49ezB9+nSnVooVTkTknNZW\n6SZoomA22EME/oorHN5T9u6772Lz5s2YMGECkpKSAEhdXixfvhyZmZkoKiqCXq/Htm3bAAAJCQnI\nzMxEQkICQkNDUVhYCM3FNS0sLEROTg7a29sxc+ZMzJgxAwCQm5uLefPmwWAwICoqCiUlJQCAYcOG\nYeXKlZgyZQoAYNWqVdBqtU6tFIMyIiL5CkQ3FTwvyIc/68LdfS1Q+4vDoOzGG2/EBTvPE+/du9fm\n+BUrVmDFihUDxk+aNAmfffbZgPGXXnqpNajrb8GCBViwYIGjIhIREZHKeCsocjYfW/PxheQe4i8h\nIiL5C8R3tT9PsOfPS10lBKujR4N7/T2hqqDMgj04ExE5j9+Z3vX000AwP5cWFwfcfLP38/VkPx0s\n7dGj7uftzR8ZqgzKiIjIN+R6RUJOgaWvOipVku++sz/N3X1ITnVs4e0yqTIok2PFEREpnZK/W+Ua\nTJLzgqEOVRmUBUPFEcndnDnA//xPoEtBwULJAaMauXMedqUOExOB//1f29MMBuCVV1xfvjuysgCT\nyXv5qTIoI6LA27sXeO+9QJeC5EjtP5wZIDrmbv33Tvf550B5ue35vvwS2LcP6OiQXhjuaxf70vcK\nBmVERORXvV7W4jUMhKi/lhbgb3+T/lfK/sGgjIiI/OrLL72fp72TrmW8Uk7KauGoNUwJN/rX1QHr\n1/tveRaqDMp48BEROU8N35lyeU0OoI7tKRevv97zv2W7Hjvm++V+9RVQX+/75fSnyqCMiIjkyxdB\ni5wCoUDfM3fqFHD4cGDL4Ii97WNrfEbGwHFvveU4n8Gm2eOoGw9HLMvyRr0zKCMiIlKRjAwgISHQ\npfA+Xwe758/7b1n2MCgjIiK/8sUJT04tZYHW3h7oErjH2Tr0VXcbvfN15alN9uhPRKrz858Dr70W\n6FKQUskpKJNTWUhZGJQRkSy0tCj3Fz4R+Z6vg91A3wsIMCgjIgp6amjZUcM6qIkvusRwRltbz/9K\n3CdCA10AIiIiT8npBCynsiiVu4FbeLjvl2HLu+/2dFTrCVW2lPGAICJSLjlcRlIyuW+/QJRvsM6F\nPdXaChw54nk+qgzKiEh55H4iIf9y9WTJH+PyIrfjWW7lsYdBGRHJBk+s5C7uO8phL0BytQ69fd+a\nHAI3BmVERKR6cjjh+gsD1IGUsk1UGZQpZeMTEZF38HtfOZwJkAPx9Ka7+bLz2EEE0y8iIiJP+Tug\nCUQA5c9lMkB0z2Dnbk/uM/RlnfDdl3YwGCOSDx6P6uSNenUmj7Y2YNs2z5dFBHjeOjcYbwV9qgrK\nLPgrhUh5GMTJnz+/W1euBObOtT2trGzg/uLrLg+UhMeS9/lrm6oyKCOiwHPnZBiMJ9Bg5Gk933ab\nd8pBvuNpEHP6tHfKYeHL7xZL3q68xNweBmVERKQ4rp70/dl6xB8XjrnbJYZcWwAt5fruu77j160D\nVqxwLS8GZUREpCgajXxP0IC8yxasnAmUT58GUlJ8XxZHGJQREZGisCVK/nzRpYUn9W5Z5vnz9uf5\n9FOgosL9ZXgDgzIioiDn7yDH3ZPy+fPAs89K6V3NI5i6xAj08n2l/3oNFvjZ2g79LzF6i7e2eah3\nspGXjo5Al4CIXMVLPjSYv/4VeOgh6X9nn74MRnI/ljwtnzvpLftHIDqldYUqW8rYtw2RMvHESo44\nuvQ0GDmccMmx/ft9vwxffMewR38ikj2eBMkeb9y3I+f9iz8uHPNH3dm71OmLZXuz9Y1BGRER+dWf\n/uR5Hrx8Sa64cEH6dDco+81vXE/jzj7JoIyIfIInSSKyxR/fDf2DL1+2ztlbn6++cj0vVd7oT0TK\nI+fLUSQ/3F/kTW431DvTUubtd18++ywwZYpr+ai2pYwHLJHysHUtMJS43fkdT6645Rbp05n95q67\nXMvbUZ7Nza7lpdqgjIiIlM/eCY9BmXL5q+5s/dhwZtnHj3u9KE7j5UsiIlI8Jbb2qZk/Ai9/XCIV\nAmhv905ezmBQRkREiuPPG7ldxQDRMW93HnvffUBbm2+W/eCDwCWXuJbGEw4vX95///2Ijo5GYmKi\ndVxzczPS0tIQHx+P9PR0tLa2Wqfl5+fDYDBg7NixKC8vt46vqqpCYmIiDAYD8vLyrOM7Ojowd+5c\nGAwGpKSk4MSJE9ZpxcXFiI+PR3x8PDZu3OiVlSUiIvmz3JQNeH75kgGSetiry7/+FTh71rk8nNlv\nes/zwQdAS4tzeXuDw6BswYIFKCsr6zOuoKAAaWlpOHLkCFJTU1FQUAAAqK6uxtatW1FdXY2ysjIs\nXrwY4uKaLVq0CEVFRTCZTDCZTNY8i4qKEBUVBZPJhKVLl2LZsmUApMBv7dq1qKysRGVlJdasWdMn\n+CMi+XP1F6mcWjoosF580fU0DL6UIxCdx7q7bGfes2rvPZvucBiU3XTTTYiMjOwzbufOncjOzgYA\nZGdnY/v27QCAHTt2ICsrC2FhYdDr9YiLi0NFRQUaGhrQ1taG5ORkAMD8+fOtaXrnNWfOHOzbtw8A\nsHv3bqSnp0Or1UKr1SItLW1AcDgYfsETKQ9PrIFx5AiQlBToUrhGzt/x3I+VwVuXwL25L7p8T1lj\nYyOio6MBANHR0WhsbAQA1NfXIyUlxTqfTqeD2WxGWFgYdDqddXxMTAzMZjMAwGw2IzY2VipIaCgi\nIiLQ1NSE+vr6PmksedmSk5MDvV4PABgyRAtgIoCpAACj0YgtW4Crr56K3/xGGgaAqVN7pnOYwxz2\nzXBXlzTc+3h0NP9XXxnx738Dd90lj/IHyzAwFe+9Bxw6ZITR6Hj+Y8eAiAjvLB9wvDxAGhaiZ/oX\nX0jlBYADB4z4wQ965j9/vmd9eqe3DLe0DL5+3hwebP18OSwFCYFbPiAd/9IoadhSHnvfB/3ry9bw\nmTN9h6VLlrbnb2w04t13B07vvT91dQEaTc/0w4dt52fZnr3z7338AMCnn9ouf3s7sHq1EcedfaRT\nDKKmpkaMHz/eOqzVavtMj4yMFEIIsWTJErF582br+NzcXFFaWioOHjwopk2bZh1/4MABcfvttwsh\nhBg/frwwm83WaaNHjxZnzpwR69evF0888YR1/OOPPy7Wr18/oGz9i9/QYGloFKK7WxoXEiINE5F/\nhYe7duzdeacQ27b5rjxkGyDEPfc4V1dPPinEo496Z5mOltdz0UiIrq6e8X/8Y8/4s2f7phk2rG86\ny9+0adLnrbd6Xm5nLV8e2PNOQkJglw8IERHR83/v+gaE2LTJdpre89hKd/vt0qdlP/jv/7Y9PyDE\n3XcLcebMwPH19T3LDA8X4p13eqZt3ix9TprUM+7aa4WYO9f+PgsIERUlRFmZ7XKMHt1/fscVE+Jc\n6NYjOjoap06dAgA0NDRg+PDhAKQWsNraWut8dXV10Ol0iImJQV1d3YDxljQnT54EAHR1deHs2bOI\niooakFdtbW2fljMiIgpucn76knzDG5eFd+3qO3zjjT3/23tpubd7+nfE5aAsIyMDxcXFAKQnJGfP\nnm0dX1JSgs7OTtTU1MBkMiE5ORkjRoxAeHg4KioqIITApk2bMGvWrAF5lZaWIjU1FQCQnp6O8vJy\ntLa2oqWlBXv27MH06dNdLSoAHqhERErGzmPVJ5B1l58vBUrSpUrfc3VdHd5TlpWVhbfeegtnzpxB\nbGws1q5di+XLlyMzMxNFRUXQ6/XYtm0bACAhIQGZmZlISEhAaGgoCgsLobkYIhYWFiInJwft7e2Y\nOXMmZsyYAQDIzc3FvHnzYDAYEBUVhZKSEgDAsGHDsHLlSky5+NKoVatWQavVurZmRKQoPMmSJwZr\nkQim/YsPGthn2Tau9NrvzNOX7qbtz2FQtmXLFpvj9+7da3P8ihUrsGLFigHjJ02ahM8++2zA+Esv\nvdQa1PW3YMECLFiwwFHxHAqmA5BILTQa4Px5YMgQIMTldnwKJq5+x3/9tdQ6Mm6cb8pDffmyt317\nlxmdWYYzaZ3Nqzd7gbDPL18SEfnS0KHA0qWBLgXZ8vXXwMXbgAOu/4lysJNfVRWQkOC78rhSFlIO\nfzfwqD4oY4sZUWC4e+x1dACffOLdspBjzgYR//u/wNatzs37+uve+f51Ng9+1/eQ+7Zwt3yuBLvu\nXM52pxXN21QflBGR8sj9pEKDy8hw/n2E7uA+Qu+/75/L0Z7cU8bLl0SkSL2/2HjC9S9fXW7zZT26\nevnSWf/4B/CHP3iWBy9f+s9//mN/mq16cKduLvb85RZXjwHFB2WHDwPffRfoUhBRf+58+fFkRv35\n+4GPn/8cWLjQszz4o8Ixf2wfb3alkp7u/XLYo/igLCtLem9bfzwoiIic46vvS2/kO2SI7/Im35Fr\n/Tj64eeLTmKD+vKlnd41iEhh5PqFTq5R4uVLb5BTWagvS934sssOT6gqKJNeVktESsegzL+UeE+Z\ns8tydd2476nbl1+6nqb3PvThhz3/33CD5+XpTxVBGQ8iInXhMe1fSmzZ4T6iXN7qPNaXy7CXT+9+\n+j74QPocOtR++qC7fNl7hXmQEikXj9/AkfM9Zc7m7a3AUokBqhx5s+6//dZ7eVm0tUmdIdvS3u5a\nXjEx9qcF3Y3+RCRP7nwpO3O/BwWPn/880CVwDwM7x1w9vj/6SPp0tfNYR/O/8Yb9aU1Nzi9nMEEZ\nlDnqmZeIlIfHrzp4Wo/d3b7L2+LOO4ELF7yfL3mfv4JdT35QekrxQRl/kRAReUauN/r3Dpa8nbfF\n3/8uvdqL/MfVuvNXC7q7D4t4s0f/UNdmlzfLhglV1VoRESmTpyfRsDDPy7Bnj2vz84e+f5WXA6dO\nOTevK/uTO69GsozrvQ8cPgw8+KDzy/WUKsIXNjcTyY8nJzce0/4l1xv9J01yPm8GUz2UdPxMnx6Y\n5draRvY6Km5sdJyXN/c9xQdlGg1w6BBw/nygS0JEnigqAr73Pel/JZ1UyHdiY+1Pk2sgSZLBOmd9\n9FEgL89/5XFGZ2egS6CCe8oA4IEHfNOJGxH5T2Qk8P3vS//zxKgO/uwSQ04C3WoX6OU7Y9cu+11S\n2OPN9bK1/zzwgH/LYIsqgjKL3htZzgcsETnG49e/vH2isdSfr+uxuxt4913pf/ZTpn5vvil9Ortf\n+eo9l64uyxWqCsosT+r03zg8yIiI/M/XLWX//Cdw442+Wwa5b7C6P3YM+NWvnMvLcg53tWVNiRQf\nlGk0wB13SJVrbyfgr24i/7Mcd5aWDEdqanxbFvIvb7WUDXZfUu97gH78Y8+W5cwyncWGAMeEkLoh\ncfXJWH9xt/7Cwz1ftuKDMgC49FLp0WlHfdoQUWDceOPgry255hrpl7MFf0ipgz/rccIE/y1L7iIi\nAl2CwPvb3xxPFwKYO9f+NHdY7on1hOKfvuzNXlDGXy1E8nfuXM//DMrUwdN6HOyeIF/sJ944XwR6\n/83NBcaNC2wZHHG1s1V36qT3MqZMAT78cOD0EDeapWyVxVb/Zu5SfEsZX0hORCQv/rjR31dBGamH\nZf84etS1dO4EV5deClxxhevp+lN8UGbR/wDt/T9byoj8z9UfTHx6Wn18eU9Z7+n19d77nuc9Zeph\neX2Wr29t0mik2zS2bvU8L1UEZf3fi8UDgkjZGJT5l7ff/WipP1+eDJuaepbT0uJZXkrY34xG1+aX\n8zp5WrbDh52b7/LLpU9HlxxtcTeG4OXLi/zxBUBE/iPnE4oa+aonc1fqMSUFeOgh5+d/8sng+SH+\nzTfAj37k/Pxy2B7uHsPR0QPH9V+fF15wLq/LLwf27bMflNnbTu6W3RvfW4oPynq3kvGLnEie7B2b\n7e3AJ5/4tyw0kLe/O925p6yiAvjXv/qOcxRclJYCra0983myDr2X442ARg5BkZz581z98stAc7P/\nlucpxQdlwMDLl0QkL/aOzfXrgYkTHc9DyuVqnfYPZgZL//rrPeks/3tKjvthMAV5g73821XHj9se\n7+7ly08/9ag4g1JFUAZIG5iXL4nkw5mTm717meR4YlQzd7b3+fNAWZnj/Hxdj11dPf+70xrir3J6\ni1LKCXi3rIMFpcuXu55nWxtw9qzr6bZssT/NG8Gz4vsps/eEl5J2XiK1O3eu56bb3nr3E8TjN3Dc\n2d7V1cBtt0k/hr11b46rJ7WYmJ50cmpNcqf/K0d6B49yWk9/GWw/cmeblJe7V5ZrrnEvnbNU01IG\n2L/pMxh3YiI5efVV2+OHDLE9nkGZ/Fm+V3u3Vll42gJ11119l2HPiBHOzecKb+Q1axZw3XWe52PR\n/2G2ceNc73tLTuT6MnCL06ftT5s927vL6k81QZkzly+9fa2aiJyj0Qx8mXBzM7B6dc+wpdWD/M+d\nk2R3t/RpKyjzJF+g5xU5g6Wvqur535Ne3+XeSts/KPvPf4D33w9ceeTGUd17O2CztX+cP++9/BUf\nlFVW9vxvLygTQnqvnuVXFRH513vvSe/j630PWe++hsLD+7aayfHESH2dPCl9WoIzW3x9+XLv3p50\ncrsi4s19mN0+OebPurdVrw89JP05+oHiLMUHZRaOusQQYvAXIhORd/X+oiwpkT4t/WF1dUl9L1n0\nP3YZlPmXO9s7L0/6dHT50l9BhNyCMl+1zvTens6+7UCO5Fy2wdgqe2oq8NJLjn+gOEsVQdlgXWL0\nf/GoyQS8+KLvy0VEfVma+auqgBkz+k6T00mVBveDH0if3rx86e79wBqNZzfXy/21fJZ1czYok+M6\neGKw9bFX9xoNcOKE98tji7svOO9PFUFZQoL0aWuHtVTmD37Qc3nEbJY6HiQi/7KcwPu3oLS1+b8s\n1MPZk3hKSs//S5dKn7ZaB9y90X/YsL7DzvZiLwQQFubasizpnBnnDm+2BoWHA0OHutZSFmjnztmf\n5s8b/b19L7m9sl+4YDso+/Zb1/JXRVC2YUPfy5eWCnr55b4bcORI6TMkpOd+CCLyH0tLma0vMFdf\nYE7+9/jjwK23Sv9///vSp7dayn71K+mpxd7sPZ3b35AhA1teA8kXLVUhIbynzB8GqztHV+RspT11\nyrXlqyIoa2qSPi0b69w54LLLgEWLeubp3ZeOEEBNTc/877zjv7ISBbPeLWX9T7hyfwKO+r7OyPLp\nTkvZpEmO83ZVSwuwcqV7aYG+y5XrU/r9gzIlHyNy7hLD3aDswgXeU9bHYO++/Oabga1jlpuOb7qJ\nv0CIvK1/FxjXXOM4KOvd4qLkE44SuXLvVv+Ay52WMlvjPTmxBvIF0v7Kmy1l9nm7s153/fGPA8dZ\nWpadJZNVsa+srAxjx46FwWDAk08+OWD6DTcA8fHS/46+HHp3Xmk5+B94QPocMsRxhFtdDRQWOlfe\nrCzg+eedm/ehh5x7NcipU8CqVc7l+fbbwLZtg893+LD0tAi55uGHHf+S3rGjp48l6is0tO/lS1v3\nnFhOZDU1fcf/+tfA738PfPedb8sYrFwJyoxG6XP+fGmcrVdluXtPmRDuvfrG1ZPyrbcCu3e7vhxn\neRJgNjcD9fUDx6uhpezkSSAuDhg+3LV0Op39aZdcEviWMkd1sX9/3yfNByProKy7uxtLlixBWVkZ\nqqursWXLFhzu3bkRgKSknh3VbLafV++d2bLRN2/u+cX+4otGfPyx7QcAnnsO+PnPHZe1qUl67L+k\nBCgqcmLlADz7rPQk6GAaGoC1a53Lc+FCYO7cwed74QVgyRLn8gwGRqPRqfn+7/96+kayZfnynvew\nHT0qBWneIIR0j6SraQLF1i/6sDDpZb533ml7+rBhPT+sZs7sO+3pp4HFi4E//cn7ZXW27tXIcvXg\no4+kTyGAX/7S/r5j64SVkdGTvj8hgF27pPt+q6t7xk+f3ne+5uaeF4pXVPSMLymRLk1+/jnwu99J\n/U3aYulyxVlvvgns3GkZMqKmBigocO2Yqa7uW9avvwb+/nfb83Z1AZ995ly+27ZJl2Lb26XGAksH\nubZaysrKpPNDb198ITVCvPoq8Npr0va3sKxfaWnPwzX79knzmkzAoUPAxx9L47u7gTNnbJdx61bn\nupk6cKDvcHIyMH686w9lOHq3ZWcnUFvrWn49jAPGDHYJ8umnpR+IvfcVy/sw7b3L9+WXpTSOzh1W\nQsbee+89MX36dOtwfn6+yM/Ptw4DEEuWCLFunRBbtlguYDr+++ADIZ59Vvr/ttuEOH9eiMsuE2Lc\nuFXiiSek8V1dQrz/vhCNjUK88UZP2hUrhEhNFaKoSIhdu3rKef68EDfd1DOfRiOlPX5ciJYWIU6d\nEuLIESHefVeIf/1Lyvurr4T4zW+EOHtWiKNHhbhwwf52aG4W4v77hfjsMyH273e8za65RirDW285\nznPZMmk+kqxatcqp+WpqhPjuO/vTP/5YiPfeE+KTT4T4xz+EmD3bK8UTnZ1CDBkiRH6+tD8MZtcu\nIYrCjzMAAAx0SURBVGbOtD/daPROuRyxdfytWiXElCnStuk/rbRUiDVrpP/vuks6PkaMEOLvf+87\n38sve7eczta9Gu3a1XfbrlwpRFiYEB0dtuc3Gu1/t/b2zTfSuGeekT6HDRNi+/ae6d3d0vh33pG+\nKw8dkoY/+kiIoUN78tRqhfjrX4XIzJSG58937nve0V9bm/S5aJH03Q2sEhUVQlx/vRAnT/bMd/68\ntB0uXOj5Lu39+cILQkycKMQf/ygNHz4sff9euCDEp58KMW5cz7wtLUKEh/fktX+/EH/5S0/+Fhcu\nCPGnPwkRFSVERoYQJSU92/aqq4Soq5P+B6TtMmmSEAsXStvP4qWXbNdNe7sQ3/ue9P/VV0vnJyGE\n+OlPhYiIEOL//T+p/levlsqxaJH9c8TIkT1lEUI6p5aX9wxbljtpku06KC31vB57/02d6m7aVW4v\n07IPW/6WLrU/b0iIVP+PPSbEYGGXRtqA8lRaWordu3fjTxd/Hm/evBkVFRXYsGEDAECjts5YiIiI\nSNUchV2hfiyHywYLumQcTxIRERG5RNb3lMXExKC218Xi2tpa6Bzd8UdERESkULIOyiZPngyTyYTj\nx4+js7MTW7duRUZGRqCLRUREROR1sr58GRoaihdffBHTp09Hd3c3cnNzMW7cuEAXi4iIiMjrZN1S\nBgC33XYbvvjiC3z55Zd47LHHnE7nqH+zZ555BiEhIWi200mYvbTNzc1IS0tDfHw80tPT0dra6t5K\nkU/Zqr/Vq1dDp9MhKSkJSUlJKCsrczotwLpXEnt1uGHDBowbNw7jx4/HsmXLXErL+lcGW/U3d+5c\n63E/atQoJCUlOZ0WYN0ria06rKysRHJyMpKSkjBlyhR8+OGHTqcFAlD/Dp/NVKiuri4xevRoUVNT\nIzo7O8V1110nqqurhRBCnDx5UkyfPl3o9XrR1NTkUtpHH31UPPnkk0IIIQoKCsSyZcv8t1LkFHv1\nt3r1avHMM8+4lVYI1r1S2KvD/fv3i2nTponOzk4hhBCnT592Oq0QrH8lcFR/Fg8//LB4/PHHXUrL\nulcGe3V4yy23iLKyMiGEEG+88YaYOnWq02mF8H/9y76lzB2VlZWIi4uDXq9HWFgY7r77buy42Ivn\nQw89hKeeesqttDt37kR2djYAIDs7G9u3b/f9ypBLHNWfGORpXda98tmrw5dffhmPPfYYwi72Wnnl\nlVc6nRZg/SuBo/oDpON/27ZtyMrKcikt614Z7NXhyJEjcfbiayJaW1sRExPjdFrA//WvyqDMbDYj\nNjbWOqzT6WA2m7Fjxw7odDpMmDChz/z19fX48Y9/7DAtADQ2NiI6OhoAEB0djUa5vrk2iDmqvw0b\nNuC6665Dbm6utQmada8u9urwyJEjOHDgAFJSUjB16lQcPHgQAOtfTRzVHwC8/fbbiI6OxujRowGw\n7tXGXh0WFBTgoYcewtVXX41HH30U+fn5AORb/6oMymz1b/bdd98hPz8fa9assY6ztJyMHDkSuy6+\ni6J/WiGEzfw0Gg07r5Uhe3WyePFi1NTU4NChQ7jqqqvw8MMPA2Ddq429eunq6kJLSws++OADPP30\n08jMzATA+leTwepky5YtuOeee6zDrHt1sVcvubm52LBhA06ePIlnn30W999/PwD51r8qgzJb/ZuN\nGjUKNTU1uO666zBq1CjU1dVh0qRJOH36tMO0dXV11ubO6OhonDp1CgDQ0NCA4a6+VZV8zl7fdlde\neaX1gHrggQdQWVk5aFrWvfLYqv+YmBjodDr89Kc/BQBMmTIFISEhaGpqcpiW9a8sjvq17Orqwmuv\nvYa5dl4MzLpXPnvHfkVFBX7yk58AAO68807Zf/erMiiz1b/ZnDlz0NjYiJqaGtTU1ECn0+Gjjz4a\nsIEd9Y2WkZGB4uJiAEBxcTFmz57t93Ujx+zVX0Ovt/a+9tprSExMdDotwLpXClt1OGvWLMyePRv7\n9+8HABw5cgSdnZ2IiooaNC3rXzkc1d/evXsxbtw4jBw50uW0rHtlsFeHcXFxeOuttwAA+/fvR3x8\nvNNpgQDUv08fIwigN954Q8THx4vRo0eLdevWDZg+atQo69OXZrNZzOz19mZ7aZuamkRqaqowGAwi\nLS1NtLS0+H5FyGW26m/evHkiMTFRTJgwQcyaNUucOnVKCMG6VyNbddjZ2Snuu+8+MX78eHH99deL\nN998UwjB+lcbe/WXk5Mj/vCHP/SZl3WvPrbq8MMPPxTJycniuuuuEykpKeKjjz4SQsi3/mX9QnIi\nIiKiYKHKy5dERERESsOgjIiIiEgGGJQRERERyQCDMiIiIiIZYFBGRH7xwgsvICEhAfPmzfP7sk+c\nOIEtW7ZYh6uqqpCXl+f3cniLXq9Hc3NzoItBRF7Gpy+JyC/GjRuHffv29ekrqqurC6GhoT5fttFo\nxDPPPIPXX3/d58vyh1GjRuHgwYMD+lojImVjSxkR+dzChQtx7NgxzJgxA1qtFvPnz8eNN96I7Oxs\nnDhxAjfffDMmTZqESZMm4f333wcgBVK33HILZs+ejdGjR2P58uXYtGkTkpOTMWHCBBw7dgwA8NVX\nX+HOO+9EcnIykpOT8d577w1Y/vLly/H2228jKSkJzz33HIxGI+644w4AwOrVq5GdnY2bb74Zer0e\n//jHP/DII49gwoQJuO2229DV1QVAal2bOnUqJk+ejBkzZlh7+bbl6NGjuO222zB58mTcfPPN+OKL\nLwAAOTk5WLhwIaZMmYIxY8ZYX/Ny7tw5LFiwABMmTMD1118Po9EIAOju7sYjjzyCxMREXHfddXjp\npZesy9iwYQMmTZqECRMmWPMnIoXzaS9oREQX6fV60dTUJFavXi0mT54szp07J4QQ4rvvvrP+f+TI\nETF58mQhhBBvvvmm0Gq14tSpU6Kjo0OMHDlSrFq1SgghxPPPPy/+f3v398reH8cB/Lmt+SCJK5Qm\nWo3MHIpysZvlR4RyseJKmAtLcufSP6BoSVEu0EpZkis34sIytWIKYxFuiTJpzbbX98JnJz/m8/18\nP/X1mXo+rs77nPf7/TrnvZtX7/fZeY+OjoqISE9Pj+zs7IiIyNXVlVRUVHyIvb29Le3t7Wp5a2tL\nLY+Pj4vVapVYLCaBQECysrJkY2NDRES6urpkbW1NotGoNDQ0yO3trYiILC8vS39//6fParPZJBQK\niYiIz+cTm80mIiK9vb3S2toqIiKhUEiKi4slEonIxMSEDAwMiIhIMBgUg8EgkUhEZmZmxG63Szwe\nFxGRu7s7dSynp6dFRGRmZkYcDsfv/ARElOb+/3UDIqKf5OfbEp2dnfjx4wcAIBqNYnh4GIFAADqd\nDqFQSK1fV1eHgoICAIDRaERLSwsAwGw2Y2trC8DLFjonJydqm3A4jKenJ2RnZ3+Im4pGo0Frayt0\nOh3MZjMSiYQap6qqCpeXlzg7O8PR0REaGxsBvMxgfbZlz+PjI3Z3d2G329Vz0WhUjZXcDN1oNKKs\nrAzBYBBerxcjIyMAAJPJhJKSEpydnWFzcxNDQ0PQal8WNfLz89U+k3t51tbWYnV19dPnI6Lvg0kZ\nEX251wnT5OQkioqKsLS0hHg8jszMTPVaMnEDAK1Wq5a1Wq26rCgi2NvbQ0ZGxh/fT7KtVquFXq9/\nEzMWi0FEUFlZmXJp9L1EIoG8vDzs7+//VmyNRgPg88Txs/PJsdDpdOpYENH3xnfKiOivenh4QGFh\nIQBgcXER8Xj8P7Vvbm6Gy+VSywcHBx/q5ObmIhwOp2z/q1m0JJPJhJubG/h8PgDA8/Mzjo+PU9bN\nzc1FaWkpPB6P2v/h4aF6vLKyAhHB+fk5Li4uUF5eDqvVCrfbDeBlw/Tr62uUl5ejqakJs7Oz6pjc\n39//670S0ffFpIyIvkRyRuj9sdPpxMLCAhRFwenpKXJyclLWe99X8prL5YLf70d1dTUqKysxNzcH\nAPD7/RgcHAQAWCwW6HQ6KIqCqampN+1fH6eKqdFooNfr4fF4MDY2BkVRUFNTo/4hIRW32435+Xko\nigKz2Yz19XW1L4PBgPr6erS1tWF2dhYZGRlwOp1IJBKwWCzo7u7GwsIC9Ho9HA4HDAYDLBYLFEV5\n81mPVGNBRN8bP4lBRPRF+vr60NHRob4PRkT0GmfKiIiIiNIAZ8qIiP7Q8PAwvF7vm3Ojo6Po7e39\nS3dERN8ZkzIiIiKiNMDlSyIiIqI0wKSMiIiIKA0wKSMiIiJKA0zKiIiIiNIAkzIiIiKiNMCkjIiI\niCgN/APmWxd4Dnjj7wAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "TCP Time-Sequence Graph"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's try to replicate the TCP Time-Sequence Graph that is known from Wireshark (Statistics > TCP Stream Analysis > Time-Sequence Graph (Stevens)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fields=[\"tcp.stream\", \"ip.src\", \"ip.dst\", \"tcp.seq\", \"tcp.ack\", \"tcp.window_size\", \"tcp.len\"]\n",
      "ts=read_pcap(\"nitroba.pcap\", fields, timeseries=True, strict=True)\n",
      "ts"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "DatetimeIndex: 81451 entries, 2008-07-22 03:51:07.095278 to 2008-07-22 08:13:47.046029\n",
        "Data columns (total 7 columns):\n",
        "tcp.stream         81451  non-null values\n",
        "ip.src             81451  non-null values\n",
        "ip.dst             81451  non-null values\n",
        "tcp.seq            81451  non-null values\n",
        "tcp.ack            81451  non-null values\n",
        "tcp.window_size    81451  non-null values\n",
        "tcp.len            81451  non-null values\n",
        "dtypes: int64(5), object(2)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 81451 entries, 2008-07-22 03:51:07.095278 to 2008-07-22 08:13:47.046029\n",
        "Data columns (total 7 columns):\n",
        "tcp.stream         81451  non-null values\n",
        "ip.src             81451  non-null values\n",
        "ip.dst             81451  non-null values\n",
        "tcp.seq            81451  non-null values\n",
        "tcp.ack            81451  non-null values\n",
        "tcp.window_size    81451  non-null values\n",
        "tcp.len            81451  non-null values\n",
        "dtypes: int64(5), object(2)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we have to select a TCP stream to analyse. As an example, we just pick stream number 10:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stream=ts[ts[\"tcp.stream\"] == 10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stream"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "DatetimeIndex: 26 entries, 2008-07-22 03:51:08.431406 to 2008-07-22 03:53:29.160668\n",
        "Data columns (total 7 columns):\n",
        "tcp.stream         26  non-null values\n",
        "ip.src             26  non-null values\n",
        "ip.dst             26  non-null values\n",
        "tcp.seq            26  non-null values\n",
        "tcp.ack            26  non-null values\n",
        "tcp.window_size    26  non-null values\n",
        "tcp.len            26  non-null values\n",
        "dtypes: int64(5), object(2)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "                            tcp.stream         ip.src         ip.dst  tcp.seq  \\\n",
        "frame.time_epoch                                                                \n",
        "2008-07-22 03:51:08.431406          10  209.85.171.97   192.168.1.64        0   \n",
        "2008-07-22 03:51:08.437600          10   192.168.1.64  209.85.171.97        1   \n",
        "2008-07-22 03:51:08.438156          10   192.168.1.64  209.85.171.97        1   \n",
        "2008-07-22 03:51:08.467383          10  209.85.171.97   192.168.1.64        1   \n",
        "2008-07-22 03:51:08.469846          10  209.85.171.97   192.168.1.64        1   \n",
        "2008-07-22 03:51:08.474440          10   192.168.1.64  209.85.171.97      154   \n",
        "2008-07-22 03:51:08.547444          10   192.168.1.64  209.85.171.97      154   \n",
        "2008-07-22 03:51:08.547498          10   192.168.1.64  209.85.171.97      421   \n",
        "2008-07-22 03:51:08.547768          10   192.168.1.64  209.85.171.97      427   \n",
        "2008-07-22 03:51:08.589823          10  209.85.171.97   192.168.1.64     1178   \n",
        "2008-07-22 03:51:08.592029          10   192.168.1.64  209.85.171.97      468   \n",
        "2008-07-22 03:51:08.594719          10   192.168.1.64  209.85.171.97      468   \n",
        "2008-07-22 03:51:08.633074          10  209.85.171.97   192.168.1.64     1225   \n",
        "2008-07-22 03:51:08.635798          10   192.168.1.64  209.85.171.97     1072   \n",
        "2008-07-22 03:51:09.295395          10   192.168.1.64  209.85.171.97     1072   \n",
        "2008-07-22 03:51:09.337628          10  209.85.171.97   192.168.1.64     2569   \n",
        "2008-07-22 03:51:09.340889          10   192.168.1.64  209.85.171.97     2096   \n",
        "2008-07-22 03:53:09.324698          10  209.85.171.97   192.168.1.64     2923   \n",
        "2008-07-22 03:53:09.561366          10  209.85.171.97   192.168.1.64     2923   \n",
        "2008-07-22 03:53:10.020463          10  209.85.171.97   192.168.1.64     2923   \n",
        "2008-07-22 03:53:10.734440          10  209.85.171.97   192.168.1.64     2923   \n",
        "2008-07-22 03:53:11.956795          10  209.85.171.97   192.168.1.64     2923   \n",
        "2008-07-22 03:53:13.662067          10  209.85.171.97   192.168.1.64     2923   \n",
        "2008-07-22 03:53:15.876856          10  209.85.171.97   192.168.1.64     2923   \n",
        "2008-07-22 03:53:20.305760          10  209.85.171.97   192.168.1.64     2923   \n",
        "2008-07-22 03:53:29.160668          10  209.85.171.97   192.168.1.64     2923   \n",
        "\n",
        "                            tcp.ack  tcp.window_size  tcp.len  \n",
        "frame.time_epoch                                               \n",
        "2008-07-22 03:51:08.431406        1             5672        0  \n",
        "2008-07-22 03:51:08.437600        1           524280        0  \n",
        "2008-07-22 03:51:08.438156        1           524280      153  \n",
        "2008-07-22 03:51:08.467383      154             6784        0  \n",
        "2008-07-22 03:51:08.469846      154             6784     1177  \n",
        "2008-07-22 03:51:08.474440     1178           523712        0  \n",
        "2008-07-22 03:51:08.547444     1178           524280      267  \n",
        "2008-07-22 03:51:08.547498     1178           524280        6  \n",
        "2008-07-22 03:51:08.547768     1178           524280       41  \n",
        "2008-07-22 03:51:08.589823      468             7872       47  \n",
        "2008-07-22 03:51:08.592029     1225           524280        0  \n",
        "2008-07-22 03:51:08.594719     1225           524280      604  \n",
        "2008-07-22 03:51:08.633074     1072             9024     1344  \n",
        "2008-07-22 03:51:08.635798     2569           523552        0  \n",
        "2008-07-22 03:51:09.295395     2569           524280     1024  \n",
        "2008-07-22 03:51:09.337628     2096            11072      354  \n",
        "2008-07-22 03:51:09.340889     2923           524280        0  \n",
        "2008-07-22 03:53:09.324698     2096            11072        0  \n",
        "2008-07-22 03:53:09.561366     2096            11072        0  \n",
        "2008-07-22 03:53:10.020463     2096            11072        0  \n",
        "2008-07-22 03:53:10.734440     2096            11072        0  \n",
        "2008-07-22 03:53:11.956795     2096            11072        0  \n",
        "2008-07-22 03:53:13.662067     2096            11072        0  \n",
        "2008-07-22 03:53:15.876856     2096            11072        0  \n",
        "2008-07-22 03:53:20.305760     2096            11072        0  \n",
        "2008-07-22 03:53:29.160668     2096            11072        0  "
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas only print the overview because the table is to wide. So we force a display:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print stream.to_string()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            tcp.stream         ip.src         ip.dst  tcp.seq  tcp.ack  tcp.window_size  tcp.len\n",
        "frame.time_epoch                                                                                                \n",
        "2008-07-22 03:51:08.431406          10  209.85.171.97   192.168.1.64        0        1             5672        0\n",
        "2008-07-22 03:51:08.437600          10   192.168.1.64  209.85.171.97        1        1           524280        0\n",
        "2008-07-22 03:51:08.438156          10   192.168.1.64  209.85.171.97        1        1           524280      153\n",
        "2008-07-22 03:51:08.467383          10  209.85.171.97   192.168.1.64        1      154             6784        0\n",
        "2008-07-22 03:51:08.469846          10  209.85.171.97   192.168.1.64        1      154             6784     1177\n",
        "2008-07-22 03:51:08.474440          10   192.168.1.64  209.85.171.97      154     1178           523712        0\n",
        "2008-07-22 03:51:08.547444          10   192.168.1.64  209.85.171.97      154     1178           524280      267\n",
        "2008-07-22 03:51:08.547498          10   192.168.1.64  209.85.171.97      421     1178           524280        6\n",
        "2008-07-22 03:51:08.547768          10   192.168.1.64  209.85.171.97      427     1178           524280       41\n",
        "2008-07-22 03:51:08.589823          10  209.85.171.97   192.168.1.64     1178      468             7872       47\n",
        "2008-07-22 03:51:08.592029          10   192.168.1.64  209.85.171.97      468     1225           524280        0\n",
        "2008-07-22 03:51:08.594719          10   192.168.1.64  209.85.171.97      468     1225           524280      604\n",
        "2008-07-22 03:51:08.633074          10  209.85.171.97   192.168.1.64     1225     1072             9024     1344\n",
        "2008-07-22 03:51:08.635798          10   192.168.1.64  209.85.171.97     1072     2569           523552        0\n",
        "2008-07-22 03:51:09.295395          10   192.168.1.64  209.85.171.97     1072     2569           524280     1024\n",
        "2008-07-22 03:51:09.337628          10  209.85.171.97   192.168.1.64     2569     2096            11072      354\n",
        "2008-07-22 03:51:09.340889          10   192.168.1.64  209.85.171.97     2096     2923           524280        0\n",
        "2008-07-22 03:53:09.324698          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n",
        "2008-07-22 03:53:09.561366          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n",
        "2008-07-22 03:53:10.020463          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n",
        "2008-07-22 03:53:10.734440          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n",
        "2008-07-22 03:53:11.956795          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n",
        "2008-07-22 03:53:13.662067          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n",
        "2008-07-22 03:53:15.876856          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n",
        "2008-07-22 03:53:20.305760          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n",
        "2008-07-22 03:53:29.160668          10  209.85.171.97   192.168.1.64     2923     2096            11072        0\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Add a column that shows who sent the packet (client or server). "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The fancy lambda expression is a function that distinguishes between the client and the server side of the stream by comparing the source IP address with the source IP address of the first packet in the stream (for TCP steams that should have been sent by the client)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stream[\"type\"] = stream.apply(lambda x: \"client\" if x[\"ip.src\"] == stream.irow(0)[\"ip.src\"] else \"server\", axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print stream.to_string()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            tcp.stream         ip.src         ip.dst  tcp.seq  tcp.ack  tcp.window_size  tcp.len    type\n",
        "frame.time_epoch                                                                                                        \n",
        "2008-07-22 03:51:08.431406          10  209.85.171.97   192.168.1.64        0        1             5672        0  client\n",
        "2008-07-22 03:51:08.437600          10   192.168.1.64  209.85.171.97        1        1           524280        0  server\n",
        "2008-07-22 03:51:08.438156          10   192.168.1.64  209.85.171.97        1        1           524280      153  server\n",
        "2008-07-22 03:51:08.467383          10  209.85.171.97   192.168.1.64        1      154             6784        0  client\n",
        "2008-07-22 03:51:08.469846          10  209.85.171.97   192.168.1.64        1      154             6784     1177  client\n",
        "2008-07-22 03:51:08.474440          10   192.168.1.64  209.85.171.97      154     1178           523712        0  server\n",
        "2008-07-22 03:51:08.547444          10   192.168.1.64  209.85.171.97      154     1178           524280      267  server\n",
        "2008-07-22 03:51:08.547498          10   192.168.1.64  209.85.171.97      421     1178           524280        6  server\n",
        "2008-07-22 03:51:08.547768          10   192.168.1.64  209.85.171.97      427     1178           524280       41  server\n",
        "2008-07-22 03:51:08.589823          10  209.85.171.97   192.168.1.64     1178      468             7872       47  client\n",
        "2008-07-22 03:51:08.592029          10   192.168.1.64  209.85.171.97      468     1225           524280        0  server\n",
        "2008-07-22 03:51:08.594719          10   192.168.1.64  209.85.171.97      468     1225           524280      604  server\n",
        "2008-07-22 03:51:08.633074          10  209.85.171.97   192.168.1.64     1225     1072             9024     1344  client\n",
        "2008-07-22 03:51:08.635798          10   192.168.1.64  209.85.171.97     1072     2569           523552        0  server\n",
        "2008-07-22 03:51:09.295395          10   192.168.1.64  209.85.171.97     1072     2569           524280     1024  server\n",
        "2008-07-22 03:51:09.337628          10  209.85.171.97   192.168.1.64     2569     2096            11072      354  client\n",
        "2008-07-22 03:51:09.340889          10   192.168.1.64  209.85.171.97     2096     2923           524280        0  server\n",
        "2008-07-22 03:53:09.324698          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n",
        "2008-07-22 03:53:09.561366          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n",
        "2008-07-22 03:53:10.020463          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n",
        "2008-07-22 03:53:10.734440          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n",
        "2008-07-22 03:53:11.956795          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n",
        "2008-07-22 03:53:13.662067          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n",
        "2008-07-22 03:53:15.876856          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n",
        "2008-07-22 03:53:20.305760          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n",
        "2008-07-22 03:53:29.160668          10  209.85.171.97   192.168.1.64     2923     2096            11072        0  client\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "client_stream=stream[stream.type == \"client\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "client_stream[\"tcp.seq\"].plot(style=\"r-o\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xa1e454c>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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66inS0tLo378/Y8eOpW/fvpUH4vPh+vSBceOgX7/TWy0iIiJSD/Xag9W9e3ea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\niIhIkP1/EGb5PsWJn1gAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that the x-axis shows the real timestamps."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For comparison, change the x-axis to be the packet number in the stream:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "client_stream.index = arange(len(client_stream))\n",
      "client_stream[\"tcp.seq\"].plot(style=\"r-o\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xa1d91ac>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Cx5UuOErcl1+2eL7jzp3S7befbqyOHm3eUF1+uTRqVOBjn0+68EJrCwcAIAK4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\n"
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looks different of course."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Bytes per stream"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "per_stream=ts.groupby(\"tcp.stream\")\n",
      "per_stream.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "MultiIndex: 9913 entries, (0, 2008-07-22 03:51:07.095278) to (2765, 2008-07-22 08:11:35.496780)\n",
        "Data columns (total 7 columns):\n",
        "tcp.stream         9913  non-null values\n",
        "ip.src             9913  non-null values\n",
        "ip.dst             9913  non-null values\n",
        "tcp.seq            9913  non-null values\n",
        "tcp.ack            9913  non-null values\n",
        "tcp.window_size    9913  non-null values\n",
        "tcp.len            9913  non-null values\n",
        "dtypes: int64(5), object(2)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "MultiIndex: 9913 entries, (0, 2008-07-22 03:51:07.095278) to (2765, 2008-07-22 08:11:35.496780)\n",
        "Data columns (total 7 columns):\n",
        "tcp.stream         9913  non-null values\n",
        "ip.src             9913  non-null values\n",
        "ip.dst             9913  non-null values\n",
        "tcp.seq            9913  non-null values\n",
        "tcp.ack            9913  non-null values\n",
        "tcp.window_size    9913  non-null values\n",
        "tcp.len            9913  non-null values\n",
        "dtypes: int64(5), object(2)"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes_per_stream = per_stream[\"tcp.len\"].sum()\n",
      "bytes_per_stream.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "tcp.stream\n",
        "0                0\n",
        "1             2565\n",
        "5             5158\n",
        "6             8266\n",
        "10            5017\n",
        "Name: tcp.len, dtype: int64"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes_per_stream.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xac810ac>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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AIDoZq7mrrq7WpEmTdOutt2r8+PF6+umnJUkNDQ3Ky8tTamqq8vPz1dTU5Guz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\nuQMAAIgiJHcAAABR5P8D+aJJK0RTZVoAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes_per_stream.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "5150771"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "biggest_stream=bytes_per_stream.idxmax()\n",
      "biggest_stream"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "88"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bytes_per_stream.ix[biggest_stream]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "5150771"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Ethernet Padding"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's have a look at the padding of the Ethernet frames. Some cards have been leaking data in the past. For more details, see\n",
      "http://www.securiteam.com/securitynews/5BP01208UO.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trailer_df = read_pcap(\"nitroba.pcap\", [\"eth.src\", \"eth.trailer\"], timeseries=True)\n",
      "trailer_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "DatetimeIndex: 95175 entries, 2008-07-22 03:51:07.095278 to 2008-07-22 08:13:47.046029\n",
        "Data columns (total 2 columns):\n",
        "eth.src        95175  non-null values\n",
        "eth.trailer    12851  non-null values\n",
        "dtypes: object(2)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 95175 entries, 2008-07-22 03:51:07.095278 to 2008-07-22 08:13:47.046029\n",
        "Data columns (total 2 columns):\n",
        "eth.src        95175  non-null values\n",
        "eth.trailer    12851  non-null values\n",
        "dtypes: object(2)"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trailer=trailer_df[\"eth.trailer\"]\n",
      "trailer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "frame.time_epoch\n",
        "2008-07-22 03:51:07.095278    NaN\n",
        "2008-07-22 03:51:07.103728    NaN\n",
        "2008-07-22 03:51:07.114897    NaN\n",
        "2008-07-22 03:51:07.139448    NaN\n",
        "2008-07-22 03:51:07.319680    NaN\n",
        "2008-07-22 03:51:07.321990    NaN\n",
        "2008-07-22 03:51:07.326517    NaN\n",
        "2008-07-22 03:51:07.335554    NaN\n",
        "2008-07-22 03:51:07.376171    NaN\n",
        "2008-07-22 03:51:07.378392    NaN\n",
        "2008-07-22 03:51:07.389299    NaN\n",
        "2008-07-22 03:51:07.390478    NaN\n",
        "2008-07-22 03:51:07.404056    NaN\n",
        "2008-07-22 03:51:07.416518    NaN\n",
        "2008-07-22 03:51:07.423663    NaN\n",
        "...\n",
        "2008-07-22 08:13:44.266370                  NaN\n",
        "2008-07-22 08:13:44.266638                  NaN\n",
        "2008-07-22 08:13:44.293692    00:00:00:00:00:00\n",
        "2008-07-22 08:13:44.585477                  NaN\n",
        "2008-07-22 08:13:44.863535                  NaN\n",
        "2008-07-22 08:13:44.873602                  NaN\n",
        "2008-07-22 08:13:44.883737                  NaN\n",
        "2008-07-22 08:13:44.893510                  NaN\n",
        "2008-07-22 08:13:44.903460                  NaN\n",
        "2008-07-22 08:13:44.913495                  NaN\n",
        "2008-07-22 08:13:44.923654                  NaN\n",
        "2008-07-22 08:13:44.933648                  NaN\n",
        "2008-07-22 08:13:44.943515                  NaN\n",
        "2008-07-22 08:13:44.953453                  NaN\n",
        "2008-07-22 08:13:47.046029                  NaN\n",
        "Name: eth.trailer, Length: 95175, dtype: object"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ok. Most frames do not seem to have padding, but some have. Let's count per value to get an overview:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trailer.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "00:00:00:00:00:00                                        7989\n",
        "3b:02:a7:19:aa:aa:03:00:80:c2:00:07:00:00:00:02:3b:02     913\n",
        "00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00:00     606\n",
        "3b:02:a7:19:00:1d:6b:99:98:6a:88:64:11:00:8f:da:00:42     303\n",
        "00:00                                                     299\n",
        "00:00:c0:a8:01:40:00:00:00:00:00:00:00:00:00:1d:d9:2e     259\n",
        "32:01:67:06:aa:aa:03:00:80:c2:00:07:00:00:00:02:3b:02     254\n",
        "2d:66:6f:6f:65:05:79:61:68:6f:6f:03:63:6f:6d:00:00:01     253\n",
        "04:67:6b:64:63:03:75:61:73:03:61:6f:6c:03:63:6f:6d:00     160\n",
        "70:03:6d:73:67:05:79:61:68:6f:6f:03:63:6f:6d:00:00:01     151\n",
        "73:6b:03:6d:61:63:03:63:6f:6d:00:00:01:00:01:00:01:00     146\n",
        "2d:66:6f:6f:62:05:79:61:68:6f:6f:03:63:6f:6d:00:00:01     101\n",
        "73:6b:03:6d:aa:aa:03:00:80:c2:00:07:00:00:00:02:3b:02      66\n",
        "72:65:76:73:aa:aa:03:00:80:c2:00:07:00:00:00:02:3b:02      54\n",
        "00:00:00:00:aa:aa:03:00:80:c2:00:07:00:00:00:02:3b:02      52\n",
        "...\n",
        "2d:66:6f:6f:aa:aa:03:00:80:c2:00:07:00:00:00:02:3b:02    1\n",
        "00:00:c9:6e:87:fc                                        1\n",
        "00:00:44:b7:84:43                                        1\n",
        "00:00:3b:fc:30:86                                        1\n",
        "00:00:7b:1f:5b:03                                        1\n",
        "00:00:78:27:f5:37                                        1\n",
        "00:00:f0:2c:e6:35                                        1\n",
        "00:00:6e:f5:46:41                                        1\n",
        "00:00:00:00:00:00:00:00:00:00:00:00:00:16:39:da:a9       1\n",
        "00:00:7a:e4:d0:27                                        1\n",
        "00:00:61:c8:85:63                                        1\n",
        "00:00:e7:99:00:70                                        1\n",
        "00:00:68:25:eb:a0                                        1\n",
        "00:00:34:ba:2b:52                                        1\n",
        "00:00:53:8a:e9:05                                        1\n",
        "Length: 635, dtype: int64"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mostly zeros, but some data. Let's decode the hex strings: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import binascii\n",
      "\n",
      "def unhex(s, sep=\":\"):\n",
      "    return binascii.unhexlify(\"\".join(s.split(sep)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s=unhex(\"3b:02:a7:19:aa:aa:03:00:80:c2:00:07:00:00:00:02:3b:02\")\n",
      "s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "';\\x02\\xa7\\x19\\xaa\\xaa\\x03\\x00\\x80\\xc2\\x00\\x07\\x00\\x00\\x00\\x02;\\x02'"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "padding = trailer_df.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "padding[\"unhex\"]=padding[\"eth.trailer\"].map(unhex)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def printable(s):\n",
      "    chars = []\n",
      "    for c in s:\n",
      "        if c.isalnum():\n",
      "            chars.append(c)\n",
      "        else:\n",
      "            chars.append(\".\")\n",
      "    return \"\".join(chars)\n",
      "           "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "printable(\"\\x95asd\\x33\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "'.asd3'"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "padding[\"printable\"]=padding[\"unhex\"].map(printable)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "padding[\"printable\"].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "......                8145\n",
        "..................    1927\n",
        "......k..j.d.....B     303\n",
        "..                     299\n",
        "2.g...............     254\n",
        ".fooe.yahoo.com...     253\n",
        ".gkdc.uas.aol.com.     160\n",
        "p.msg.yahoo.com...     151\n",
        "sk.mac.com........     148\n",
        ".foob.yahoo.com...     101\n",
        "sk.m..............      66\n",
        "revs..............      54\n",
        "ge.w..............      45\n",
        "1.1...............      44\n",
        ".goo..............      42\n",
        "...\n",
        "..........Wz......    1\n",
        "..M...                1\n",
        "...i.Z                1\n",
        "..x...                1\n",
        "..N...                1\n",
        "..n.oN                1\n",
        "....fK                1\n",
        "....fk                1\n",
        "..Y8..                1\n",
        "..n.FA                1\n",
        "...O.r                1\n",
        "....Qn                1\n",
        "..PK.e                1\n",
        "...w..                1\n",
        "..1...                1\n",
        "Length: 375, dtype: int64"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ratio_printable(s):\n",
      "    printable = sum(1.0 for c in s if c.isalnum())\n",
      "    return printable / len(s)         "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ratio_printable(\"a\\x93sdfs\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "0.8333333333333334"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "padding[\"ratio_printable\"] = padding[\"unhex\"].map(ratio_printable)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "padding[padding[\"ratio_printable\"] > 0.5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&ltclass 'pandas.core.frame.DataFrame'&gt\n",
        "DatetimeIndex: 727 entries, 2008-07-22 03:51:20.018817 to 2008-07-22 05:40:13.338449\n",
        "Data columns (total 5 columns):\n",
        "eth.src            727  non-null values\n",
        "eth.trailer        727  non-null values\n",
        "unhex              727  non-null values\n",
        "printable          727  non-null values\n",
        "ratio_printable    727  non-null values\n",
        "dtypes: float64(1), object(4)\n",
        "</pre>"
       ],
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "DatetimeIndex: 727 entries, 2008-07-22 03:51:20.018817 to 2008-07-22 05:40:13.338449\n",
        "Data columns (total 5 columns):\n",
        "eth.src            727  non-null values\n",
        "eth.trailer        727  non-null values\n",
        "unhex              727  non-null values\n",
        "printable          727  non-null values\n",
        "ratio_printable    727  non-null values\n",
        "dtypes: float64(1), object(4)"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "_.printable.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        ".fooe.yahoo.com...    253\n",
        ".gkdc.uas.aol.com.    160\n",
        "p.msg.yahoo.com...    151\n",
        ".foob.yahoo.com...    101\n",
        ".weather.com......     31\n",
        "ge.weather.com....     26\n",
        "1.1..HOST.239.255.      1\n",
        "..CDWW                  1\n",
        ".foof.yahoo.com...      1\n",
        "..3rbo                  1\n",
        "..BIKM                  1\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now find out which Ethernet cards sent those packets with more than 50% ASCII data in their padding:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "padding[padding[\"ratio_printable\"] > 0.5]['eth.src'].drop_duplicates()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "frame.time_epoch\n",
        "2008-07-22 03:51:20.018817    00:1d:d9:2e:4f:61\n",
        "2008-07-22 04:10:14.155085    00:1d:6b:99:98:68\n",
        "Name: eth.src, dtype: object"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML('<iframe src=http://www.coffer.com/mac_find/?string=00%3A1d%3Ad9%3A2e%3A4f%3A61 width=600 height=300></iframe>')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe src=http://www.coffer.com/mac_find/?string=00%3A1d%3Ad9%3A2e%3A4f%3A61 width=600 height=300></iframe>"
       ],
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "<IPython.core.display.HTML at 0xae4fe2c>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Thats 'Hon Hai Precision' (and \"Netopia Inc\" for the other MAC address)."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}