2014-challenge6.ipynb

   1 {
   2  "metadata": {
   3   "name": "",
   4   "signature": "sha256:5973034ac2337d403853a03b7c1d5fdb5ce28d6b3ac7e6be40537ee382e9f648"
   5  },
   6  "nbformat": 3,
   7  "nbformat_minor": 0,
   8  "worksheets": [
   9   {
  10    "cells": [
  11     {
  12      "cell_type": "code",
  13      "collapsed": false,
  14      "input": [
  15       "import matplotlib.pyplot as plt\n",
  16       "import pandas as pd\n",
  17       "import collections\n",
  18       "import string\n",
  19       "%matplotlib inline\n",
  20       "\n",
  21       "from cipherbreak import *\n",
  22       "\n",
  23       "c6a = open('2014/6a.ciphertext').read()\n",
  24       "c6b = open('2014/6b.ciphertext').read()"
  25      ],
  26      "language": "python",
  27      "metadata": {},
  28      "outputs": [],
  29      "prompt_number": 1
  30     },
  31     {
  32      "cell_type": "code",
  33      "collapsed": false,
  34      "input": [
  35       "freqs = pd.Series(english_counts)\n",
  36       "freqs.plot(kind='bar')"
  37      ],
  38      "language": "python",
  39      "metadata": {},
  40      "outputs": [
  41       {
  42        "metadata": {},
  43        "output_type": "pyout",
  44        "prompt_number": 2,
  45        "text": [
  46         "<matplotlib.axes.AxesSubplot at 0x7fc55c2b98d0>"
  47        ]
  48       },
  49       {
  50        "metadata": {},
  51        "output_type": "display_data",
  52        "png": 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8t6ulZdPJtO0b/rb51H+o7RyTO8bmtS1vm5H4GWgnm6SO4VTgReBJt90NrAd2AOuArljZ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\n",
  53        "text": [
  54         "<matplotlib.figure.Figure at 0x7fc586677dd8>"
  55        ]
  56       }
  57      ],
  58      "prompt_number": 2
  59     },
  60     {
  61      "cell_type": "code",
  62      "collapsed": false,
  63      "input": [
  64       "freqs_6a = pd.Series(collections.Counter([l.lower() for l in c6a if l in string.ascii_letters]))\n",
  65       "freqs_6a.plot(kind='bar')"
  66      ],
  67      "language": "python",
  68      "metadata": {},
  69      "outputs": [
  70       {
  71        "metadata": {},
  72        "output_type": "pyout",
  73        "prompt_number": 3,
  74        "text": [
  75         "<matplotlib.axes.AxesSubplot at 0x7fc55a6375c0>"
  76        ]
  77       },
  78       {
  79        "metadata": {},
  80        "output_type": "display_data",
  81        "png": 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  82        "text": [
  83         "<matplotlib.figure.Figure at 0x7fc55a6462b0>"
  84        ]
  85       }
  86      ],
  87      "prompt_number": 3
  88     },
  89     {
  90      "cell_type": "code",
  91      "collapsed": false,
  92      "input": [
  93       "freqs_6b = pd.Series(collections.Counter([l.lower() for l in c6b if l in string.ascii_letters]))\n",
  94       "freqs_6b.plot(kind='bar')"
  95      ],
  96      "language": "python",
  97      "metadata": {},
  98      "outputs": [
  99       {
 100        "metadata": {},
 101        "output_type": "pyout",
 102        "prompt_number": 4,
 103        "text": [
 104         "<matplotlib.axes.AxesSubplot at 0x7fc55a642128>"
 105        ]
 106       },
 107       {
 108        "metadata": {},
 109        "output_type": "display_data",
 110        "png": 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j4qTsq+lhN6s9Y0h13MRomtY2RdHDFhGpOXrYI4ijh12NJtW2aVqd2TbpavSwRURvuSbo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\ngg==\n",
 111        "text": [
 112         "<matplotlib.figure.Figure at 0x7fc55a5a5400>"
 113        ]
 114       }
 115      ],
 116      "prompt_number": 4
 117     },
 118     {
 119      "cell_type": "code",
 120      "collapsed": false,
 121      "input": [
 122       "c6as = sanitise(c6a)\n",
 123       "c6as"
 124      ],
 125      "language": "python",
 126      "metadata": {},
 127      "outputs": [
 128       {
 129        "metadata": {},
 130        "output_type": "pyout",
 131        "prompt_number": 5,
 132        "text": [
 133         "'mtaeglatcleptenopeautelebiiootatwnantateituiiagaeostgvetabdresiacqobwavgrhrsihssaekajbwwttdrsmeetnyafsegilegtkrreocuantteomsgstnsiaeluutrbaiaeteeserhxgtooarrbhpcklialhnaesvearhbepiydcesewtaxuyaerywoeinhteegeisieireaassrbitnhtuorooleewsttereoahyakhlsmsaeodslthsutigqimnidsgetpmwtrnnotfhvselkaumrndvcnrluceryhyeetlnigouncnanrhpnosbhshpslreclvrinfoehniaeennhcrbenrgunruesmlrehiutgteordroeaeoisoeusiknteeslohthdcrmisuteoteaeoshfaiaesemritrseisaigwyrmhrbtetncoenuhorcadeodlcrncomnctosihudtcinagesntisutigytmshthyalatlsnhilguimtlbfldyhrfrnetsaosteetaefhlgokhretcakuteihrlrtlsetshlcpeadhthyutaeennhryraeennihrnbhnsnehyutsdtoywmtiatalwhvbepetlxihuscrtadtikhnxmsaesnwluevgnrcpegvnhteruigeuealsdntikeaeomctwrybusiilephkyodhrsyhecaatrmrltrarretstuoetnuesiduaidoesisaeetbllerpntroisiatsiasesomihsieiaunsaitneelacrfnrnngvetteenslhvpepteonedtnaooutgsotancetimiiwoetiuihclsewtcniieotslfbeecohenpoelsdoctceeemiiirttmhbiuovecegaitjuaborcleentatruyinetsidlaeehitwencceohwvohoatwkteroarhcseer'"
 134        ]
 135       }
 136      ],
 137      "prompt_number": 5
 138     },
 139     {
 140      "cell_type": "code",
 141      "collapsed": false,
 142      "input": [
 143       "c6bs = sanitise(c6b)\n",
 144       "c6bs"
 145      ],
 146      "language": "python",
 147      "metadata": {},
 148      "outputs": [
 149       {
 150        "metadata": {},
 151        "output_type": "pyout",
 152        "prompt_number": 6,
 153        "text": [
 154         "'hwssswxfewhhrfewpdrvttdhxbccleayphalnadhiehaoudrotwnrrvysabjlttbaytmelrkaidopthatlelrtwaamaneksvvzrvllatkcrjquicizgtoqcpnrrkttowandqehtqrvtbaydqealannohulanuzlwextlvjrvivhnohdqmgykaclmswrupdetfioftfelhzpxhaswftwprrsweiseohefpdrvttnvagdvswgoerbetnharvaeevtlltbmgaiatgelinmdawevhatterdhrznbnvoutnefoteveaehlaymhacglzeptvvdimworfisgtuzlwibeqohubtghamqornjnnrumqvjtxeltfovgawdaeevllgrtxibgtibevmpsaateoasevaeyqohameonncfuidoefafattemuimnflznbekofobrliaehhauihnnnwzaeevtlltpaalnanvtzlzuucptaelinanpaahewfthaosetaribnbnvhaevdhyytlmuxb'"
 155        ]
 156       }
 157      ],
 158      "prompt_number": 6
 159     },
 160     {
 161      "cell_type": "code",
 162      "collapsed": false,
 163      "input": [
 164       "key_a, score = railfence_break(c6as)\n",
 165       "key_a, score"
 166      ],
 167      "language": "python",
 168      "metadata": {},
 169      "outputs": [
 170       {
 171        "metadata": {},
 172        "output_type": "pyout",
 173        "prompt_number": 7,
 174        "text": [
 175         "(3, -2314.997881051078)"
 176        ]
 177       }
 178      ],
 179      "prompt_number": 7
 180     },
 181     {
 182      "cell_type": "code",
 183      "collapsed": false,
 184      "input": [
 185       "' '.join(segment(railfence_decipher(c6as, key_a)))"
 186      ],
 187      "language": "python",
 188      "metadata": {},
 189      "outputs": [
 190       {
 191        "metadata": {},
 192        "output_type": "pyout",
 193        "prompt_number": 8,
 194        "text": [
 195         "'mark the last message told usa lot the scuttling equipment is designed to pump water in and out of the vessel like a submarine dive control but clearly they werent planning to turn a container ship into a sub this ship is a largescale version of something i have seen in the caribbean drug runners use a similar technique to get below radar coverage for inshore runs sinking the vessel so that the deck remains just below the wave tops the fda pirates seem more interested in staying away from shore but getting close enough to track and record electronic communications without detection i am guessing this scuttling system is what they call nautilus in their log but i am still baffled by the references to seahorse the next page of the log looks harder to crack but the cipher clerk tells me it is a hill cipher and that they must have been in a hurry or have been enciphering by hand since they just used a two by two matrix actually we have been pretty lax with our security and i think the next message is end will use avi genere cipher given that we are using secure cables i dont think we have too much to worry about so i will keep the keyword short say three characters more later harry'"
 196        ]
 197       }
 198      ],
 199      "prompt_number": 8
 200     },
 201     {
 202      "cell_type": "code",
 203      "collapsed": false,
 204      "input": [
 205       "key_b, score = hill_break(c6bs)\n",
 206       "key_b, score"
 207      ],
 208      "language": "python",
 209      "metadata": {},
 210      "outputs": [
 211       {
 212        "metadata": {},
 213        "output_type": "pyout",
 214        "prompt_number": 14,
 215        "text": [
 216         "(matrix([[0, 1],\n",
 217         "         [1, 1]]), -666.1299098341699)"
 218        ]
 219       }
 220      ],
 221      "prompt_number": 14
 222     },
 223     {
 224      "cell_type": "code",
 225      "collapsed": false,
 226      "input": [
 227       "' '.join(segment(hill_decipher(key_b, c6bs)))"
 228      ],
 229      "language": "python",
 230      "metadata": {},
 231      "outputs": [
 232       {
 233        "metadata": {},
 234        "output_type": "pyout",
 235        "prompt_number": 13,
 236        "text": [
 237         "'phase six seahorse operated exactly as planned with good forward visibility at the trial depths the crew managed several tasks requiring concentration and dexterity and we plan to run a full test overnight on dummy cables dropped from the ship the software seems to be operating as designed but there are still bugs in the firmware that need ironing out before we deploy the collective is working full time to hunt them down and remove them though we are all getting tired mistakes are easy to make and could be fatal time is no longer on our side though and we are still planning to launch the final phase of the operation in three days timex'"
 238        ]
 239       }
 240      ],
 241      "prompt_number": 13
 242     },
 243     {
 244      "cell_type": "code",
 245      "collapsed": false,
 246      "input": [],
 247      "language": "python",
 248      "metadata": {},
 249      "outputs": []
 250     }
 251    ],
 252    "metadata": {}
 253   }
 254  ]
 255 }