{
 "metadata": {
  "name": "pandasAuthLogAnalysis"
 }, 
 "nbformat": 2, 
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# this program takes the auth.log and gives you a report with ipython and pandas"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 47
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# read contents of auth.log line by line into 'data' variable"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 14
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# import pandas"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 14
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# create pandas DataFrame with 'data'"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 14
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "data = open('auth.log').readlines()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 5
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "import pandas"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 6
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "frame = pandas.DataFrame([x.split() for x in data])"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 15
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "# looks for all logs with 'Failed password for root'"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 48
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "mylist=[]", 
      "def get_bruteRoot(data):", 
      "\tfor line in data:", 
      "\t\tif 'Failed password for root' in line:", 
      "\t\t\tmylist.append(line)", 
      "\tframe = pandas.DataFrame([x.split() for x in mylist])", 
      "\treturn frame[10].value_counts()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 16
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "mylist=[]", 
      "get_bruteRoot(data).plot(kind='bar')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 37, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xb6d638c>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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kAEBeXh4CAgJgaWmJ3r17Iz8/X1h+2bJlsLGxgaOjI+Li4sQK+zXJxQ5AMrRhzFcTOA8q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\nTkSuQmCC\n"
      }
     ], 
     "prompt_number": 37
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "mylist=[]", 
      "def get_failedPasswordInvalidUser(data):", 
      "\tfor line in data:", 
      "\t\tif 'Failed password for invalid user' in line:", 
      "\t\t\tmylist.append(line)", 
      "\tfailedframe = pandas.DataFrame([x.split() for x in mylist])", 
      "\treturn failedframe[10].value_counts()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 18
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "mylist=[]", 
      "get_failedPasswordInvalidUser(data) #.plot(kind='bar')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 50, 
       "text": [
        "test          75", 
        "admin         59", 
        "alex          43", 
        "sarah         36", 
        "cyrus         34", 
        "guest         32", 
        "emma          32", 
        "user          30", 
        "sabine        30", 
        "renee         30", 
        "philippine    30", 
        "pauline       30", 
        "olivia        30", 
        "madison       30", 
        "madeline      30", 
        "...", 
        "bthadm          1", 
        "boavista        1", 
        "bluetulippon    1", 
        "asterisk        1", 
        "anonftproot     1", 
        "anita           1", 
        "anda            1", 
        "anamaria        1", 
        "alien           1", 
        "ale             1", 
        "admosfer        1", 
        "admins          1", 
        "accounts        1", 
        "LK              1", 
        "Horizon         1", 
        "Length: 903"
       ]
      }
     ], 
     "prompt_number": 50
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "def get_loginHistory(data):", 
      "\tfor line in data:", 
      "\t\tif 'Failed password for' in line:", 
      "\t\t\tmylist.append(line)", 
      "\tframe = pandas.DataFrame([x.split() for x in mylist])", 
      "\treturn frame[8].value_counts()"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": 51
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "mylist=[]", 
      "get_loginHistory(data).plot(kind='bar')"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 52, 
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xb6165d4c>"
       ]
      }, 
      {
       "output_type": "display_data", 
       "png": 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tbW34+flh+vTp6Ny5M959912cP39eJftoYmIibG1tceTIEQQFBQn7p5eXFxwd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\n"
      }
     ], 
     "prompt_number": 52
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "mylist=[]", 
      "get_loginHistory(data)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 53, 
       "text": [
        "invalid    2708", 
        "root       1894", 
        "mysql      15", 
        "backup     8", 
        "user2      4", 
        "user3      2", 
        "proxy      2", 
        "news       2", 
        "mail       2", 
        "games      2", 
        "user1      1"
       ]
      }
     ], 
     "prompt_number": 53
    }, 
    {
     "cell_type": "code", 
     "collapsed": false, 
     "input": [
      "mylist=[]", 
      "get_failedPasswordInvalidUser(data)"
     ], 
     "language": "python", 
     "outputs": [
      {
       "output_type": "pyout", 
       "prompt_number": 44, 
       "text": [
        "test          75", 
        "admin         59", 
        "alex          43", 
        "sarah         36", 
        "cyrus         34", 
        "guest         32", 
        "emma          32", 
        "user          30", 
        "sabine        30", 
        "renee         30", 
        "philippine    30", 
        "pauline       30", 
        "olivia        30", 
        "madison       30", 
        "madeline      30", 
        "...", 
        "bthadm          1", 
        "boavista        1", 
        "bluetulippon    1", 
        "asterisk        1", 
        "anonftproot     1", 
        "anita           1", 
        "anda            1", 
        "anamaria        1", 
        "alien           1", 
        "ale             1", 
        "admosfer        1", 
        "admins          1", 
        "accounts        1", 
        "LK              1", 
        "Horizon         1", 
        "Length: 903"
       ]
      }
     ], 
     "prompt_number": 44
    }, 
    {
     "cell_type": "code", 
     "collapsed": true, 
     "input": [
      "# end of tutorial"
     ], 
     "language": "python", 
     "outputs": [], 
     "prompt_number": " "
    }
   ]
  }
 ]
}