2014-challenge7.ipynb

   1 {
   2  "metadata": {
   3   "name": "",
   4   "signature": "sha256:3a4749bad5690e746e43cddb887ed31282398e12c90470d01010a2e6a024c527"
   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       "c7a = open('2014/7a.ciphertext').read()\n",
  24       "c7b = open('2014/7b.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 0x7f51e347e1d0>"
  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 0x7f521042a0f0>"
  55        ]
  56       }
  57      ],
  58      "prompt_number": 2
  59     },
  60     {
  61      "cell_type": "code",
  62      "collapsed": false,
  63      "input": [
  64       "freqs_7a = pd.Series(collections.Counter([l.lower() for l in c7a if l in string.ascii_letters]))\n",
  65       "freqs_7a.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 0x7f51e3496278>"
  76        ]
  77       },
  78       {
  79        "metadata": {},
  80        "output_type": "display_data",
  81        "png": 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19fuwKY1NCJbHZvDtNuLyQyGEEL4YS+SZv8KspxgjRjyN3X4O0cSIEaZJq59DNP4xLHvX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\n/Os4hwAAAABJRU5ErkJggg==\n",
  82        "text": [
  83         "<matplotlib.figure.Figure at 0x7f51e349fda0>"
  84        ]
  85       }
  86      ],
  87      "prompt_number": 3
  88     },
  89     {
  90      "cell_type": "code",
  91      "collapsed": false,
  92      "input": [
  93       "freqs_7b = pd.Series(collections.Counter([l.lower() for l in c7b if l in string.ascii_letters]))\n",
  94       "freqs_7b.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 0x7f51e33869e8>"
 105        ]
 106       },
 107       {
 108        "metadata": {},
 109        "output_type": "display_data",
 110        "png": 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+YYplXZqRx6WMjPx84FDgMOCdwC2Eif1aYHXymNXANTmfX5KUUlHnkbdeStcCqwinH56Y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\nSVqk/h/e+2Rlj0saNgAAAABJRU5ErkJggg==\n",
 111        "text": [
 112         "<matplotlib.figure.Figure at 0x7f51e339d7b8>"
 113        ]
 114       }
 115      ],
 116      "prompt_number": 4
 117     },
 118     {
 119      "cell_type": "code",
 120      "collapsed": false,
 121      "input": [
 122       "c7as = sanitise(c7a)\n",
 123       "c7as"
 124      ],
 125      "language": "python",
 126      "metadata": {},
 127      "outputs": [
 128       {
 129        "metadata": {},
 130        "output_type": "pyout",
 131        "prompt_number": 5,
 132        "text": [
 133         "'veyjmyjkrzilysyjeydorulcsrcjnmoiddeugurlogfsnwcrlhckhghmwkejxlyktlagflalvtmgkyyomvlmgkyjosfdrzepwaqgnrzeqzinoaqforkcsltjwdussrzarlhcnajneqzabbakeebatjgoiklgcerzebjidlwmgdussnmljwdgftmlhceeazalasksnbtlmukcvtfwilderhrckuksbjqtfwcpwwfsdydrcsdwsbyfdmfeblhcneqkejohguhussjmciqfmjuqoirzosltfwsfapuwwmmlbzatwhyvnmadcstfstrzedvafsdzwelgpcjaraneanrzeqwwylepkscshmjscaslglmfgcjakqsrwrwlhcuurswyqollhcktyjbmsrbkibwcjwapwdyfapwamxapgulvfgnekwtcjsqiuyjeuatfsdgktgfcravcharlepfodtojlsdssrwncvtmjegffmjccvdcukndarwsgkaukokwtfanediiwtfasmfaqmbpwsamekasqaolscmmpjwodqeyjsyyouzellhcqfgltcvajgcydsfapuatfsjsjypagewdgfsnwcraolkyqleklhcveacpjstckcyfcyjrwscpsncveqaglwdrgdchlmqaljotsrceorwonwrylebnefacjwdckiefebxopmnbwrqwamhepstggnqawykajjeyvyagnawrlwdytosltfwrcxepwnawtmlhcuazdeqanrzejssrhaplodlhcxdydoeturlhcfevlscutggnfsskwrcsljqwmjrgwdgliqwnajynleboirzakgrckeamrceobafgwdyesagtpsnqhoqatggnaapfwryfdrwljkuqohyltfwyuwrcjeydlwmprgwfstgvollulvepktyfdgkhmotfwwfglcssqwmzdygkpmoepwdrzeqgrrgfagmnmtgfgrzeweuqlbcvogfggkrcsljqillelkitwalvwmmlbtupftfjosyhytarlepqilvawkillhyltgeerzegjillepuenlmgyhrforuaruhyfyrzilyuqwfsdbsltfwyasnfsrbdyfsvczihsciwdydoaslqgciwtgftfwmgvdjwodlhcgccsnasnwguewtkwaazaplsfgwgfgrzebwenkeyuazdeqanrzepwgggngvolliksggferzeskwgdlzwanjozdekturatksylwebkokwdghlmeaaqtmyerlhcxujdcmnepsgceankfpgmrzemealagmnepfmcftgxiyergyhratgkillhcarzwsranrwrcktqlondawslmfguwajdhyneydorlojgsczepw'"
 134        ]
 135       }
 136      ],
 137      "prompt_number": 5
 138     },
 139     {
 140      "cell_type": "code",
 141      "collapsed": false,
 142      "input": [
 143       "c7bs = sanitise(c7b)\n",
 144       "c7bs"
 145      ],
 146      "language": "python",
 147      "metadata": {},
 148      "outputs": [
 149       {
 150        "metadata": {},
 151        "output_type": "pyout",
 152        "prompt_number": 6,
 153        "text": [
 154         "'anwaecnndrtwtanireoahsrdntieerdctewayerevaaarpioerobsedescrehlaoitithsdtrinosepeplthtnidtwsduledlhotpeaterhaaredeoaiodtahregerothwhtsureteeinwigiresetpowicooonitseudseechacteofiileonewiotacteodteuneahsedaptryemronmomlexontkemeoyvitsteenhantrertieineotndlhpoplyitewitrsedeevdycfrmnsnaypinmteeneeinahinepritedehveleaorelllvmocnahncepherohjuuvehautathttetoasipowwneedselopgdeslfedterfcaatehjasdtrprleseeretetecneorrsgckamiiwaefutiashaongcthsrenrehtrsthhaceinwtprloghetodaeraloeedeatsomldwedtwefsbelevpdteonoignsavftegsebomcehatietnrsptonthusorecredendcetlwaehesedrncveracuhoihaleinaemeunherdesttstasipedeyleeemtiisiigothntssfolnitretrwhemtkhhswtorcssnererohteencsapeblircnthuleofenrsromrnshmaddaodeatihioroutwevthapeceetovterdleaditewdottttcatkmbhtheicwantnisedwatxankeabovoanmswlprgaispodfogndpedeswrdacttrefhdesyaberretlavod'"
 155        ]
 156       }
 157      ],
 158      "prompt_number": 6
 159     },
 160     {
 161      "cell_type": "code",
 162      "collapsed": false,
 163      "input": [
 164       "key_a, score = vigenere_frequency_break(c7as)\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         "('say', -1726.4679903722085)"
 176        ]
 177       }
 178      ],
 179      "prompt_number": 7
 180     },
 181     {
 182      "cell_type": "code",
 183      "collapsed": false,
 184      "input": [
 185       "' '.join(segment(vigenere_decipher(c7as, key_a)))"
 186      ],
 187      "language": "python",
 188      "metadata": {},
 189      "outputs": [
 190       {
 191        "metadata": {},
 192        "output_type": "pyout",
 193        "prompt_number": 8,
 194        "text": [
 195         "'dear mark things area lot clearer now i flew out to inspect the ship myself last night and took a good look around the reason the ship was not scuttled was that the valves had jammed it looks like the driftwood was pulled into the mechanism and blocked the inlet presumably the crew had already abandoned the vessel which was lucky for us without the ship we would have had no idea that the fda had been operating in these waters seahorse is no longer a mystery the cutaway on the starboard side cleared an area of around five meters square with a distinctive pattern of bolts fastened to reinforced deck plates i saw something like this on a sub rescue mission a couple of years ago when they fitted a local ship with a jury rigged inspection system the deck plates can carry a crane designed to deploy an rova remote operated vehicle designed for undersea operations i was already concerned about the reference to the cables in the last part of the fda log but the next section has me really worried it is encrypted with a more secure modified amsco transposition cipher and tells us what they were really up to what i dont understand is how the whole assembly is powered the sort of computing they must be doing is really intensive and would burn through a battery in days in that time their intercept might not catch anything useful but they can hardly have hijacked a local socket in the middle of the ocean can you get me a chart showing the deepsea cables in the region i dont imagine the us will be a problem but it may need some diplomacy to get the full coverage maps from the omani government if i am right it is in their best interests to playalong we all have alot to lose here'"
 196        ]
 197       }
 198      ],
 199      "prompt_number": 8
 200     },
 201     {
 202      "cell_type": "code",
 203      "collapsed": false,
 204      "input": [
 205       "key_b, score = amsco_break(c7bs)\n",
 206       "key_b, score"
 207      ],
 208      "language": "python",
 209      "metadata": {},
 210      "outputs": [
 211       {
 212        "metadata": {},
 213        "output_type": "pyout",
 214        "prompt_number": 9,
 215        "text": [
 216         "(((1, 2, 0, 4, 3), (2, 1)), -1902.8377732825452)"
 217        ]
 218       }
 219      ],
 220      "prompt_number": 9
 221     },
 222     {
 223      "cell_type": "code",
 224      "collapsed": false,
 225      "input": [
 226       "' '.join(segment(amsco_transposition_decipher(c7bs, key_b[0], fillpattern=key_b[1])))"
 227      ],
 228      "language": "python",
 229      "metadata": {},
 230      "outputs": [
 231       {
 232        "metadata": {},
 233        "output_type": "pyout",
 234        "prompt_number": 10,
 235        "text": [
 236         "'phase seven we approached the cable junction undercover of night with nautilus at an elevation of three feet towing seahorse to starboard comms interception showed that we remained undetected and seahorse was deployed at operating depth the various layers of armoured protection were removed from the cable and as expected once the steel jacket was removed the other layers provided little resistance the divers entered the water and cut into the core to insert the optical repeaters linking them back to the man in the middle unit which was powered up and fully tested initial tests showed that it was operating as expected and three keys have already been recovered from the omani transmissions with daylight approaching the remaining tests were postponed for the following night and the ship returned to deeper waters where it remained at low deck height the divers were left at seahorse to decompress slowly and will be recovered tomorrow once the final tests have been concluded'"
 237        ]
 238       }
 239      ],
 240      "prompt_number": 10
 241     },
 242     {
 243      "cell_type": "code",
 244      "collapsed": false,
 245      "input": [
 246       "transpositions[key_b[0]]"
 247      ],
 248      "language": "python",
 249      "metadata": {},
 250      "outputs": [
 251       {
 252        "metadata": {},
 253        "output_type": "pyout",
 254        "prompt_number": 12,
 255        "text": [
 256         "['cable',\n",
 257         " 'facto',\n",
 258         " 'facts',\n",
 259         " 'gabon',\n",
 260         " 'hafts',\n",
 261         " 'hefts',\n",
 262         " 'ibexs',\n",
 263         " 'kabul',\n",
 264         " 'lacys',\n",
 265         " 'ladys',\n",
 266         " 'laius',\n",
 267         " 'lefts',\n",
 268         " 'macon',\n",
 269         " 'macro',\n",
 270         " 'macys',\n",
 271         " 'malts',\n",
 272         " 'melon',\n",
 273         " 'melts',\n",
 274         " 'negro',\n",
 275         " 'oahus',\n",
 276         " 'obeys',\n",
 277         " 'obits',\n",
 278         " 'odets',\n",
 279         " 'pacts',\n",
 280         " 'pants',\n",
 281         " 'pelts',\n",
 282         " 'pints',\n",
 283         " 'piotr',\n",
 284         " 'pious',\n",
 285         " 'plots',\n",
 286         " 'plows',\n",
 287         " 'ploys',\n",
 288         " 'rafts',\n",
 289         " 'rants',\n",
 290         " 'remus',\n",
 291         " 'rents',\n",
 292         " 'riots',\n",
 293         " 'scout',\n",
 294         " 'shout',\n",
 295         " 'snout',\n",
 296         " 'cabbed',\n",
 297         " 'cabbie',\n",
 298         " 'cabbys',\n",
 299         " 'cabral',\n",
 300         " 'dabble',\n",
 301         " 'faeroe',\n",
 302         " 'gabbro',\n",
 303         " 'ibexes',\n",
 304         " 'jaguar',\n",
 305         " 'kaboom',\n",
 306         " 'kaftan',\n",
 307         " 'lacuna',\n",
 308         " 'lagoon',\n",
 309         " 'lefter',\n",
 310         " 'legume',\n",
 311         " 'macaws',\n",
 312         " 'magyar',\n",
 313         " 'malays',\n",
 314         " 'maltas',\n",
 315         " 'mellon',\n",
 316         " 'negevs',\n",
 317         " 'nellys',\n",
 318         " 'nelson',\n",
 319         " 'odious',\n",
 320         " 'paddys',\n",
 321         " 'panzas',\n",
 322         " 'peggys',\n",
 323         " 'pelves',\n",
 324         " 'pennys',\n",
 325         " 'photos',\n",
 326         " 'pinups',\n",
 327         " 'qantas',\n",
 328         " 'rabats',\n",
 329         " 'rallys',\n",
 330         " 'refuse',\n",
 331         " 'refute',\n",
 332         " 'remuss',\n",
 333         " 'renews',\n",
 334         " 'repute',\n",
 335         " 'scouts',\n",
 336         " 'shouts',\n",
 337         " 'snouts',\n",
 338         " 'cabbage',\n",
 339         " 'cabrera',\n",
 340         " 'dabbled',\n",
 341         " 'gadwall',\n",
 342         " 'ladonna',\n",
 343         " 'leftest',\n",
 344         " 'madonna',\n",
 345         " 'malayan',\n",
 346         " 'million',\n",
 347         " 'papacys',\n",
 348         " 'pellets',\n",
 349         " 'penneys',\n",
 350         " 'qantass',\n",
 351         " 'ragtags',\n",
 352         " 'refuses',\n",
 353         " 'refuter',\n",
 354         " 'regrets',\n",
 355         " 'rennets',\n",
 356         " 'renters',\n",
 357         " 'reroute',\n",
 358         " 'sallust',\n",
 359         " 'macassar',\n",
 360         " 'mahatmas',\n",
 361         " 'mahayana',\n",
 362         " 'nanettes',\n",
 363         " 'palatals',\n",
 364         " 'phosphor',\n",
 365         " 'reenters',\n",
 366         " 'phosphors',\n",
 367         " 'sinusitis',\n",
 368         " 'malayalams',\n",
 369         " 'sinusitiss']"
 370        ]
 371       }
 372      ],
 373      "prompt_number": 12
 374     },
 375     {
 376      "cell_type": "code",
 377      "collapsed": false,
 378      "input": [],
 379      "language": "python",
 380      "metadata": {},
 381      "outputs": []
 382     }
 383    ],
 384    "metadata": {}
 385   }
 386  ]
 387 }