├── .gitattributes ├── .gitignore ├── AttackAesSbox.ipynb ├── AttackDesRoundXor.ipynb ├── LICENSE ├── README.md ├── Trace.py ├── aes.py ├── attackaessbox.py ├── attackdesroundxor.py ├── compareperformance.py ├── condaveraes.py ├── condaverdes.py ├── cpavisualdemo.py ├── data ├── aesinvsbox.npy ├── aessbox.npy └── bytehammingweight.npy ├── desutils.py ├── howto ├── HOWTO.md ├── howto-notebook-des-result.png ├── howto-notebook-des-settings.png ├── howto-notebook-des.png ├── howto-notebooks.png └── howto-script-aes-result.png ├── lracpa.py ├── results ├── attackaessbox_test.png ├── attackaessbox_test.txt ├── performance_logs.txt └── performance_logs_bis.txt ├── traces ├── hwdes_card8_power.npz └── swaes_atmega_power.trs └── trs2npz.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # configuration for git-lfs 2 | traces/*.trs filter=lfs diff=lfs merge=lfs -text 3 | traces/*.npz filter=lfs diff=lfs merge=lfs -text 4 | howto/*.png filter=lfs diff=lfs merge=lfs -text 5 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | traces/* 2 | results/* 3 | misc/* 4 | .ipynb_checkpoints/* 5 | *.pyc 6 | leakage-modelling-tutorial/* 7 | -------------------------------------------------------------------------------- /AttackAesSbox.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# CPA and LRA on the AES S-box" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html\n", 15 | "\n", 16 | "Author: Ilya Kizhvatov\n", 17 | "\n", 18 | "Version: 1.0, 2017-05-14\n", 19 | "\n", 20 | "The code should be self-explanatory (especially if you look into lracpa.py module).\n", 21 | "\n", 22 | "In the plots:\n", 23 | "- red trace is for known correct candidate\n", 24 | "- blue trace is for the winning candidate (e.g. the one with maximum peak)\n", 25 | "- grey traces are for all other candidates" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "## 1. Configure the environment" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": null, 38 | "metadata": { 39 | "collapsed": true 40 | }, 41 | "outputs": [], 42 | "source": [ 43 | "# we will be plotting figures right here and not in a separate window\n", 44 | "%matplotlib inline \n", 45 | "\n", 46 | "# generic python stuff\n", 47 | "import matplotlib\n", 48 | "import matplotlib.pyplot as plt\n", 49 | "import numpy as np # make sure you use numpy-MKL build for adequate performance!\n", 50 | "import time\n", 51 | "\n", 52 | "# configure figure size\n", 53 | "matplotlib.rcParams['figure.figsize'] = (15.0, 10.0)\n", 54 | "\n", 55 | "# local packages\n", 56 | "from aes import AES # interweb's SlowAES toolbox\n", 57 | "from lracpa import * # my LRA-CPA toolbox\n", 58 | "from condaveraes import * # incremental conditional averaging" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": null, 64 | "metadata": { 65 | "collapsed": true 66 | }, 67 | "outputs": [], 68 | "source": [ 69 | "# launch sideby console for manual insight into results\n", 70 | "%qtconsole" 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": {}, 76 | "source": [ 77 | "## 2. Attack settings" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": { 84 | "collapsed": true 85 | }, 86 | "outputs": [], 87 | "source": [ 88 | "## Traceset, number of traces, and S-box to attack\n", 89 | "tracesetFilename = \"traces/swaes_atmega_power.npz\"\n", 90 | "sampleRange = (900, 1200) # range of samples to attack, in the format (low, high)\n", 91 | "N = 100 # number of traces to attack (less or equal to the amount of traces in the file)\n", 92 | "offset = 0 # trace number to start from\n", 93 | "evolutionStep = 10 # step for intermediate reports\n", 94 | "SboxNum = 2 # S-box to attack, counting from 0\n", 95 | "\n", 96 | "## Leakage model\n", 97 | "## (these parameters correspond to function names in lracpa module)\n", 98 | "intermediateFunction = sBoxOut # for CPA and LRA\n", 99 | "leakageFunction = leakageModelHW # for CPA\n", 100 | "basisFunctionsModel = basisModelSingleBits # for LRA\n", 101 | "\n", 102 | "## Known key for ranking\n", 103 | "knownKeyStr = \"2B7E151628AED2A6ABF7158809CF4F3C\".decode(\"hex\") # the correct key\n", 104 | "encrypt = True # to avoid selective commenting in the following lines below " 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": null, 110 | "metadata": { 111 | "collapsed": true 112 | }, 113 | "outputs": [], 114 | "source": [ 115 | "# get the round key\n", 116 | "if encrypt: # for encryption, the first round key is as is\n", 117 | " knownKey = np.array(map(ord, knownKeyStr), dtype=\"uint8\")\n", 118 | "else: # for decryption, need to run key expansion \n", 119 | " expandedKnownKey = AES().expandKey(map(ord, knownKeyStr), 16, 16 * 11) # this returs a list\n", 120 | " knownKey = np.array(expandedKnownKey[176-16:177], dtype=\"uint8\")\n", 121 | "print \"Known roundkey : 0x%s\" % str(bytearray(knownKey)).encode(\"hex\")" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "## 3. Load samples and data" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": null, 134 | "metadata": { 135 | "collapsed": true 136 | }, 137 | "outputs": [], 138 | "source": [ 139 | "# Readout\n", 140 | "print \"Loading \" + tracesetFilename\n", 141 | "t0 = time.clock()\n", 142 | "npzfile = np.load(tracesetFilename)\n", 143 | "data = npzfile['data'][offset:offset + N,SboxNum] # selecting only the required byte\n", 144 | "traces = npzfile['traces'][offset:offset + N,sampleRange[0]:sampleRange[1]]\n", 145 | "t1 = time.clock()\n", 146 | "timeLoad = t1 - t0\n", 147 | "\n", 148 | "# Log traceset parameters\n", 149 | "(numTraces, traceLength) = traces.shape\n", 150 | "print \"Number of traces loaded :\", numTraces\n", 151 | "print \"Trace length :\", traceLength\n", 152 | "print \"Loading time : %0.2f s\" % timeLoad" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": null, 158 | "metadata": { 159 | "collapsed": true 160 | }, 161 | "outputs": [], 162 | "source": [ 163 | "# dump some data\n", 164 | "data[0:10]" 165 | ] 166 | }, 167 | { 168 | "cell_type": "markdown", 169 | "metadata": {}, 170 | "source": [ 171 | "## 4. LRA and CPA with intermediate snapshots" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": null, 177 | "metadata": { 178 | "collapsed": true 179 | }, 180 | "outputs": [], 181 | "source": [ 182 | "t0 = time.clock()\n", 183 | "\n", 184 | "# initialize the incremental averager\n", 185 | "CondAver = ConditionalAveragerAesSbox(256, traceLength)\n", 186 | "\n", 187 | "# allocate arrays for storing key rank evolution\n", 188 | "numSteps = int(np.ceil(N / np.double(evolutionStep)))\n", 189 | "keyRankEvolutionCPA = np.zeros(numSteps)\n", 190 | "keyRankEvolutionLRA = np.zeros(numSteps)\n", 191 | "\n", 192 | "# the incremental loop\n", 193 | "tracesToSkip = 20 # warm-up to avoid numerical problems for small evolution step\n", 194 | "for i in range (0, tracesToSkip - 1):\n", 195 | " CondAver.addTrace(data[i], traces[i])\n", 196 | "for i in range(tracesToSkip - 1, N):\n", 197 | " CondAver.addTrace(data[i], traces[i])\n", 198 | "\n", 199 | " if (((i + 1) % evolutionStep == 0) or ((i + 1) == N)):\n", 200 | "\n", 201 | " (avdata, avtraces) = CondAver.getSnapshot()\n", 202 | " \n", 203 | " CorrTraces = cpaAES(avdata, avtraces, intermediateFunction, leakageFunction)\n", 204 | " R2, coefs = lraAES(avdata, avtraces, intermediateFunction, basisFunctionsModel)\n", 205 | "\n", 206 | " print \"---\\nResults after %d traces\" % (i + 1)\n", 207 | " print \"CPA\"\n", 208 | " CorrPeaks = np.max(np.abs(CorrTraces), axis=1) # global maximization, absolute value!\n", 209 | " CpaWinningCandidate = np.argmax(CorrPeaks)\n", 210 | " CpaWinningCandidatePeak = np.max(CorrPeaks)\n", 211 | " CpaCorrectCandidateRank = np.count_nonzero(CorrPeaks >= CorrPeaks[knownKey[SboxNum]])\n", 212 | " CpaCorrectCandidatePeak = CorrPeaks[knownKey[SboxNum]]\n", 213 | " print \"Winning candidate: 0x%02x, peak magnitude %f\" % (CpaWinningCandidate, CpaWinningCandidatePeak)\n", 214 | " print \"Correct candidate: 0x%02x, peak magnitude %f, rank %d\" % (knownKey[SboxNum], CpaCorrectCandidatePeak, CpaCorrectCandidateRank)\n", 215 | "\n", 216 | " print \"LRA\"\n", 217 | " R2Peaks = np.max(R2, axis=1) # global maximization\n", 218 | " LraWinningCandidate = np.argmax(R2Peaks)\n", 219 | " LraWinningCandidatePeak = np.max(R2Peaks)\n", 220 | " LraCorrectCandidateRank = np.count_nonzero(R2Peaks >= R2Peaks[knownKey[SboxNum]])\n", 221 | " LraCorrectCandidatePeak = R2Peaks[knownKey[SboxNum]]\n", 222 | " print \"Winning candidate: 0x%02x, peak magnitude %f\" % (LraWinningCandidate, LraWinningCandidatePeak)\n", 223 | " print \"Correct candidate: 0x%02x, peak magnitude %f, rank %d\" % (knownKey[SboxNum], LraCorrectCandidatePeak, LraCorrectCandidateRank)\n", 224 | "\n", 225 | " stepCount = int(np.floor(i / np.double(evolutionStep)))\n", 226 | " keyRankEvolutionCPA[stepCount] = CpaCorrectCandidateRank\n", 227 | " keyRankEvolutionLRA[stepCount] = LraCorrectCandidateRank\n", 228 | "\n", 229 | "t1 = time.clock()\n", 230 | "timeAll = t1 - t0\n", 231 | "\n", 232 | "print \"---\\nCumulative timing\"\n", 233 | "print \"%0.2f s\" % timeAll" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": null, 239 | "metadata": { 240 | "collapsed": true 241 | }, 242 | "outputs": [], 243 | "source": [ 244 | "# save the rank evolution for later processing\n", 245 | "np.savez(\"results/AES-keyRankEvolutionSbox%02d\" % SboxNum, kreCPA=keyRankEvolutionCPA, kreLRA=keyRankEvolutionLRA, step=evolutionStep)" 246 | ] 247 | }, 248 | { 249 | "cell_type": "markdown", 250 | "metadata": {}, 251 | "source": [ 252 | "## 5. Visualize results" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": null, 258 | "metadata": { 259 | "collapsed": true 260 | }, 261 | "outputs": [], 262 | "source": [ 263 | "fig = plt.figure()\n", 264 | "\n", 265 | "# allocate grid\n", 266 | "axCPA = plt.subplot2grid((3, 2), (0, 0))\n", 267 | "axLRA = plt.subplot2grid((3, 2), (1, 0))\n", 268 | "axLRAcoefs = plt.subplot2grid((3, 2), (2, 0))\n", 269 | "axRankEvolution = plt.subplot2grid((2, 2), (0, 1), rowspan = 3)\n", 270 | "\n", 271 | "# compute trace nubmers for x axis (TODO: move into block 3)\n", 272 | "traceNumbers = np.arange(evolutionStep, N + 1, evolutionStep)\n", 273 | "\n", 274 | "# CPA\n", 275 | "axCPA.plot(CorrTraces.T, color = 'grey')\n", 276 | "if CpaWinningCandidate != knownKey[SboxNum]:\n", 277 | " axCPA.plot(CorrTraces[CpaWinningCandidate, :], 'blue')\n", 278 | "axCPA.plot(CorrTraces[knownKey[SboxNum], :], 'r')\n", 279 | "axRankEvolution.plot(traceNumbers, keyRankEvolutionCPA, color = 'green')\n", 280 | "axCPA.set_xlim([0, traceLength])\n", 281 | "\n", 282 | "# LRA\n", 283 | "axLRA.plot(R2.T, color = 'grey')\n", 284 | "if LraWinningCandidate != knownKey[SboxNum]:\n", 285 | " axLRA.plot(R2[LraWinningCandidate, :], 'blue')\n", 286 | "axLRA.plot(R2[knownKey[SboxNum], :], 'r')\n", 287 | "axRankEvolution.plot(traceNumbers, keyRankEvolutionLRA, color = 'magenta')\n", 288 | "axLRA.set_xlim([0, traceLength])\n", 289 | "\n", 290 | "# LRA coefs\n", 291 | "coefsKnownKey = np.array(coefs[knownKey[SboxNum]])\n", 292 | "axLRAcoefs.pcolormesh(coefsKnownKey[:,:-1].T, cmap=\"jet\")\n", 293 | "axLRAcoefs.set_xlim([0, traceLength])\n", 294 | "\n", 295 | "# labels\n", 296 | "fig.suptitle(\"CPA and LRA on %d traces\" % N)\n", 297 | "axCPA.set_ylabel('Correlation')\n", 298 | "axLRA.set_ylabel('R2')\n", 299 | "axLRAcoefs.set_ylabel('Basis function (bit)')\n", 300 | "axLRAcoefs.set_xlabel('Time sample')\n", 301 | "axRankEvolution.set_ylabel('Correct key candidate rank')\n", 302 | "axRankEvolution.set_xlabel('Number of traces')\n", 303 | "axRankEvolution.set_title('Correct key rank evolution (global maximisation)')\n", 304 | "\n", 305 | "# Limits and tick labels for key rand evolution plot\n", 306 | "axRankEvolution.set_xlim([traceNumbers[int(np.ceil(tracesToSkip / np.double(evolutionStep))) - 1], N])\n", 307 | "axRankEvolution.set_ylim([0, 256])\n", 308 | "axRankEvolution.grid(b=True, which='both', color='0.65',linestyle='-')\n", 309 | "#axRankEvolution.ticklabel_format(style='sci', axis='x', scilimits=(0,0), useOffset=True)\n", 310 | "\n", 311 | "# Legend for rank evolution plot\n", 312 | "axRankEvolution.legend(['CPA', 'LRA'], loc='upper right')\n", 313 | "\n", 314 | "plt.show()" 315 | ] 316 | } 317 | ], 318 | "metadata": { 319 | "kernelspec": { 320 | "display_name": "Python 2", 321 | "language": "python", 322 | "name": "python2" 323 | }, 324 | "language_info": { 325 | "codemirror_mode": { 326 | "name": "ipython", 327 | "version": 2 328 | }, 329 | "file_extension": ".py", 330 | "mimetype": "text/x-python", 331 | "name": "python", 332 | "nbconvert_exporter": "python", 333 | "pygments_lexer": "ipython2", 334 | "version": "2.7.13" 335 | } 336 | }, 337 | "nbformat": 4, 338 | "nbformat_minor": 1 339 | } 340 | -------------------------------------------------------------------------------- /AttackDesRoundXor.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# LRA and CPA on DES round XOR (per S-box)\n", 8 | "\n", 9 | "This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html\n", 10 | "\n", 11 | "Author: Ilya Kizhvatov\n", 12 | "\n", 13 | "Version: 1.0, 2017-05-14\n", 14 | "\n", 15 | "The code should be self-explanatory.\n", 16 | "\n", 17 | "The attack uses conditional averaging for performance. Note how for DES this requires splitting the intermediate variable computation into two parts: part for averaging and part for the final intermediate value compuation." 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "## 1. Configure the environment" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": null, 30 | "metadata": { 31 | "collapsed": true 32 | }, 33 | "outputs": [], 34 | "source": [ 35 | "# we will be plotting figures right here and not in a separate window\n", 36 | "%matplotlib inline \n", 37 | "\n", 38 | "# generic python stuff\n", 39 | "import matplotlib\n", 40 | "import matplotlib.pyplot as plt\n", 41 | "import numpy as np # make sure you use numpy-MKL build for adequate performance!\n", 42 | "import struct\n", 43 | "import time\n", 44 | "\n", 45 | "# configure figure size\n", 46 | "matplotlib.rcParams['figure.figsize'] = (15.0, 10.0)\n", 47 | "\n", 48 | "# local packages\n", 49 | "from desutils import * # my DES utilities\n", 50 | "from lracpa import * # my LRA-CPA toolbox\n", 51 | "from condaverdes import * # incremental conditional averaging" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": null, 57 | "metadata": { 58 | "collapsed": true 59 | }, 60 | "outputs": [], 61 | "source": [ 62 | "# lauch sideby console for manual insight into results\n", 63 | "%qtconsole" 64 | ] 65 | }, 66 | { 67 | "cell_type": "markdown", 68 | "metadata": {}, 69 | "source": [ 70 | "## 2. Attack settings" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": null, 76 | "metadata": { 77 | "collapsed": true 78 | }, 79 | "outputs": [], 80 | "source": [ 81 | "## Traceset, number of traces, and S-box to attack\n", 82 | "tracesetFilename = \"traces/hwdes_card8_power.npz\"\n", 83 | "sampleRange = (0, 50) # range of smaples to attack\n", 84 | "N = 10000 # number of traces to attack (not more that nthe file has)\n", 85 | "offset = 0 # trace number to start from\n", 86 | "evolutionStep = 500 # step for intermediate reports\n", 87 | "SboxNum = 1 # S-box to attack, counting from 0\n", 88 | "\n", 89 | "## Leakage model\n", 90 | "## (these parameters correspond to function names in lracpa module)\n", 91 | "averagingFunction = roundXOR_valueForAveraging # for CPA and LRA\n", 92 | "intermediateFunction = roundXOR_targetVariable # for CPA and LRA\n", 93 | "leakageFunction = leakageModelHW # for CPA\n", 94 | "basisFunctionsModel = basisModelSingleBits # for LRA\n", 95 | "\n", 96 | "## Known key for ranking\n", 97 | "knownKey = 0x8A7400A03230DA28 # the correct key\n", 98 | "encrypt = True" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": null, 104 | "metadata": { 105 | "collapsed": true 106 | }, 107 | "outputs": [], 108 | "source": [ 109 | "# get the known round key\n", 110 | "roundKeyNum = 1\n", 111 | "if (encrypt == False):\n", 112 | " roundKeyNum = 16\n", 113 | "roundKey = computeRoundKeys(knownKey, roundKeyNum)[roundKeyNum-1]\n", 114 | "knownKeyChunk = roundKeyChunk(roundKey, SboxNum)\n", 115 | "print \"Known round key: \" + format(roundKey, '#014x'),\n", 116 | "print '[',\n", 117 | "for i in range(8):\n", 118 | " print format(roundKeyChunk(roundKey, i), '#04x'),\n", 119 | "print ']'" 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": {}, 125 | "source": [ 126 | "## 3. Load samples and data" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": null, 132 | "metadata": { 133 | "collapsed": true 134 | }, 135 | "outputs": [], 136 | "source": [ 137 | "# Readout\n", 138 | "print \"Loading \" + tracesetFilename\n", 139 | "t0 = time.clock()\n", 140 | "npzfile = np.load(tracesetFilename)\n", 141 | "data = npzfile['data'][0:N]\n", 142 | "traces = npzfile['traces'][0:N,sampleRange[0]:sampleRange[1]]\n", 143 | "t1 = time.clock()\n", 144 | "timeLoad = t1 - t0\n", 145 | "\n", 146 | "# convert data byte arrays to integers (more convenient for DES)\n", 147 | "datanew = []\n", 148 | "for i in range(0, len(data)):\n", 149 | " datanew.append(struct.unpack('!Q', data[i][0:8].tostring())[0])\n", 150 | "data = datanew # old data will be garbage-collected\n", 151 | "\n", 152 | "# Log traceset parameters\n", 153 | "(numTraces, traceLength) = traces.shape\n", 154 | "print \"Number of traces loaded :\", numTraces\n", 155 | "print \"Trace length :\", traceLength\n", 156 | "print \"Loading time : %0.2f s\" % timeLoad" 157 | ] 158 | }, 159 | { 160 | "cell_type": "markdown", 161 | "metadata": {}, 162 | "source": [ 163 | "## 4. Attack with a fixed number of traces" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": null, 169 | "metadata": { 170 | "collapsed": true 171 | }, 172 | "outputs": [], 173 | "source": [ 174 | "# perform conditional averaging\n", 175 | "CondAver = ConditionalAveragerDes(1024, traceLength)\n", 176 | "for i in range(N):\n", 177 | " CondAver.addTrace(data[i], traces[i], averagingFunction, SboxNum)\n", 178 | "(avdata, avtraces) = CondAver.getSnapshot()\n", 179 | "\n", 180 | "# CPA\n", 181 | "CorrTraces = cpaDES(avdata, avtraces, intermediateFunction, SboxNum, leakageFunction)\n", 182 | "\n", 183 | "# LRA\n", 184 | "R2, coefs = lraDES(avdata, avtraces, intermediateFunction, SboxNum, basisFunctionsModel)\n", 185 | "\n", 186 | "### visualize results\n", 187 | "\n", 188 | "fig = plt.figure()\n", 189 | "\n", 190 | "# allocate grid\n", 191 | "axCPA = plt.subplot2grid((3, 1), (0, 0))\n", 192 | "axLRA = plt.subplot2grid((3, 1), (1, 0))\n", 193 | "axLRAcoefs = plt.subplot2grid((3, 1), (2, 0))\n", 194 | "\n", 195 | "# CPA\n", 196 | "axCPA.plot(CorrTraces.T, color = 'grey')\n", 197 | "axCPA.plot(CorrTraces[knownKeyChunk, :], 'r')\n", 198 | "axCPA.set_xlim([0, traceLength])\n", 199 | "\n", 200 | "# LRA\n", 201 | "axLRA.plot(R2.T, color = 'grey')\n", 202 | "axLRA.plot(R2[knownKeyChunk, :], 'r')\n", 203 | "axLRA.set_xlim([0, traceLength])\n", 204 | "\n", 205 | "# LRA coefs\n", 206 | "coefsKnownKey = np.array(coefs[knownKeyChunk])\n", 207 | "axLRAcoefs.pcolormesh(coefsKnownKey[:,:-1].T, cmap=\"jet\")\n", 208 | "axLRAcoefs.set_xlim([0, traceLength])\n", 209 | "\n", 210 | "# labels\n", 211 | "fig.suptitle(\"CPA and LRA on %d traces\" % N)\n", 212 | "axCPA.set_ylabel('Correlation')\n", 213 | "axLRA.set_ylabel('R2')\n", 214 | "axLRAcoefs.set_ylabel('Basis function (bit)')\n", 215 | "axLRAcoefs.set_xlabel('Time sample')\n", 216 | "\n", 217 | "plt.show()" 218 | ] 219 | }, 220 | { 221 | "cell_type": "markdown", 222 | "metadata": {}, 223 | "source": [ 224 | "## 5. Attack with evolving number of traces" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": null, 230 | "metadata": { 231 | "collapsed": true 232 | }, 233 | "outputs": [], 234 | "source": [ 235 | "t0 = time.clock()\n", 236 | "\n", 237 | "# initialize the incremental averager\n", 238 | "CondAver = ConditionalAveragerDes(1024, traceLength)\n", 239 | "\n", 240 | "# allocate arrays for storing key rank evolution\n", 241 | "numSteps = int(np.ceil(N / np.double(evolutionStep)))\n", 242 | "keyRankEvolutionCPA = np.zeros(numSteps)\n", 243 | "keyRankEvolutionLRA = np.zeros(numSteps)\n", 244 | "\n", 245 | "# the incremental loop\n", 246 | "tracesToSkip = 20 # warm-up to avoid numerical problems for small evolution step\n", 247 | "for i in range (0, tracesToSkip - 1):\n", 248 | " CondAver.addTrace(data[i], traces[i], averagingFunction, SboxNum)\n", 249 | "for i in range(tracesToSkip - 1, N):\n", 250 | " CondAver.addTrace(data[i], traces[i], averagingFunction, SboxNum)\n", 251 | "\n", 252 | " if (((i + 1) % evolutionStep == 0) or ((i + 1) == N)):\n", 253 | "\n", 254 | " (avdata, avtraces) = CondAver.getSnapshot()\n", 255 | " \n", 256 | " CorrTraces = cpaDES(avdata, avtraces, intermediateFunction, SboxNum, leakageFunction)\n", 257 | " R2, coefs = lraDES(avdata, avtraces, intermediateFunction, SboxNum, basisFunctionsModel)\n", 258 | " #R2 = normalizeR2Traces(R2)\n", 259 | "\n", 260 | " print \"---\\nResults after %d traces\" % (i + 1)\n", 261 | " print \"CPA\"\n", 262 | " CorrPeaks = np.max(np.abs(CorrTraces), axis=1) # global maximization, absolute value!\n", 263 | " CpaWinningCandidate = np.argmax(CorrPeaks)\n", 264 | " CpaWinningCandidatePeak = np.max(CorrPeaks)\n", 265 | " CpaCorrectCandidateRank = np.count_nonzero(CorrPeaks >= CorrPeaks[knownKeyChunk])\n", 266 | " CpaCorrectCandidatePeak = CorrPeaks[knownKeyChunk]\n", 267 | " print \"Winning candidate: 0x%02x, peak magnitude %f\" % (CpaWinningCandidate, CpaWinningCandidatePeak)\n", 268 | " print \"Correct candidate: 0x%02x, peak magnitude %f, rank %d\" % (knownKeyChunk, CpaCorrectCandidatePeak, CpaCorrectCandidateRank)\n", 269 | "\n", 270 | " print \"LRA\"\n", 271 | " R2Peaks = np.max(R2, axis=1) # global maximization\n", 272 | " LraWinningCandidate = np.argmax(R2Peaks)\n", 273 | " LraWinningCandidatePeak = np.max(R2Peaks)\n", 274 | " LraCorrectCandidateRank = np.count_nonzero(R2Peaks >= R2Peaks[knownKeyChunk])\n", 275 | " LraCorrectCandidatePeak = R2Peaks[knownKeyChunk]\n", 276 | " print \"Winning candidate: 0x%02x, peak magnitude %f\" % (LraWinningCandidate, LraWinningCandidatePeak)\n", 277 | " print \"Correct candidate: 0x%02x, peak magnitude %f, rank %d\" % (knownKeyChunk, LraCorrectCandidatePeak, LraCorrectCandidateRank)\n", 278 | "\n", 279 | " stepCount = int(np.floor(i / np.double(evolutionStep)))\n", 280 | " keyRankEvolutionCPA[stepCount] = CpaCorrectCandidateRank\n", 281 | " keyRankEvolutionLRA[stepCount] = LraCorrectCandidateRank\n", 282 | "\n", 283 | "t1 = time.clock()\n", 284 | "timeAll = t1 - t0\n", 285 | "\n", 286 | "print \"---\\nCumulative timing\"\n", 287 | "print \"%0.2f s\" % timeAll" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": null, 293 | "metadata": { 294 | "collapsed": true 295 | }, 296 | "outputs": [], 297 | "source": [ 298 | "# save the rank evolution for later processing\n", 299 | "np.savez(\"results/keyRankEvolutionSbox%02d\" % SboxNum, kreCPA=keyRankEvolutionCPA, kreLRA=keyRankEvolutionLRA, step=evolutionStep)" 300 | ] 301 | }, 302 | { 303 | "cell_type": "markdown", 304 | "metadata": {}, 305 | "source": [ 306 | "## 7. Visualize results" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": null, 312 | "metadata": { 313 | "collapsed": true 314 | }, 315 | "outputs": [], 316 | "source": [ 317 | "fig = plt.figure()\n", 318 | "\n", 319 | "# allocate grid\n", 320 | "axCPA = plt.subplot2grid((3, 2), (0, 0))\n", 321 | "axLRA = plt.subplot2grid((3, 2), (1, 0))\n", 322 | "axLRAcoefs = plt.subplot2grid((3, 2), (2, 0))\n", 323 | "axRankEvolution = plt.subplot2grid((2, 2), (0, 1), rowspan = 3)\n", 324 | "\n", 325 | "# compute trace nubmers for x axis (TODO: move into block 3)\n", 326 | "traceNumbers = np.arange(evolutionStep, N + 1, evolutionStep)\n", 327 | "\n", 328 | "# CPA\n", 329 | "axCPA.plot(CorrTraces.T, color = 'grey')\n", 330 | "if CpaWinningCandidate != knownKeyChunk:\n", 331 | " axCPA.plot(CorrTraces[CpaWinningCandidate, :], 'blue')\n", 332 | "axCPA.plot(CorrTraces[knownKeyChunk, :], 'r')\n", 333 | "axRankEvolution.plot(traceNumbers, keyRankEvolutionCPA, color = 'green')\n", 334 | "axCPA.set_xlim([0, traceLength])\n", 335 | "\n", 336 | "# LRA\n", 337 | "axLRA.plot(R2.T, color = 'grey')\n", 338 | "if LraWinningCandidate != knownKeyChunk:\n", 339 | " axLRA.plot(R2[LraWinningCandidate, :], 'blue')\n", 340 | "axLRA.plot(R2[knownKeyChunk, :], 'r')\n", 341 | "axRankEvolution.plot(traceNumbers, keyRankEvolutionLRA, color = 'magenta')\n", 342 | "axLRA.set_xlim([0, traceLength])\n", 343 | "\n", 344 | "# LRA coefs\n", 345 | "coefsKnownKey = np.array(coefs[knownKeyChunk])\n", 346 | "axLRAcoefs.pcolormesh(coefsKnownKey[:,:-1].T, cmap=\"jet\")\n", 347 | "axLRAcoefs.set_xlim([0, traceLength])\n", 348 | "\n", 349 | "# labels\n", 350 | "fig.suptitle(\"CPA and LRA on %d traces\" % N)\n", 351 | "axCPA.set_ylabel('Correlation')\n", 352 | "axLRA.set_ylabel('R2')\n", 353 | "axLRAcoefs.set_ylabel('Basis function (bit)')\n", 354 | "axLRAcoefs.set_xlabel('Time sample')\n", 355 | "axRankEvolution.set_ylabel('Correct key candidate rank')\n", 356 | "axRankEvolution.set_xlabel('Number of traces')\n", 357 | "axRankEvolution.set_title('Correct key rank evolution (global maximisation)')\n", 358 | "\n", 359 | "# Limits and tick labels for key rand evolution plot\n", 360 | "axRankEvolution.set_xlim([traceNumbers[int(np.ceil(tracesToSkip / np.double(evolutionStep))) - 1], N])\n", 361 | "axRankEvolution.set_ylim([0, 64])\n", 362 | "axRankEvolution.grid(b=True, which='both', color='0.65',linestyle='-')\n", 363 | "#axRankEvolution.ticklabel_format(style='sci', axis='x', scilimits=(0,0), useOffset=True)\n", 364 | "\n", 365 | "# Legend for rank evolution plot\n", 366 | "axRankEvolution.legend(['CPA', 'LRA'], loc='upper right')\n", 367 | "\n", 368 | "plt.show()" 369 | ] 370 | } 371 | ], 372 | "metadata": { 373 | "kernelspec": { 374 | "display_name": "Python 2", 375 | "language": "python", 376 | "name": "python2" 377 | }, 378 | "language_info": { 379 | "codemirror_mode": { 380 | "name": "ipython", 381 | "version": 2 382 | }, 383 | "file_extension": ".py", 384 | "mimetype": "text/x-python", 385 | "name": "python", 386 | "nbconvert_exporter": "python", 387 | "pygments_lexer": "ipython2", 388 | "version": "2.7.13" 389 | } 390 | }, 391 | "nbformat": 4, 392 | "nbformat_minor": 1 393 | } 394 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Pysca toolbox 2 | 3 | This toolbox was started in 2014 to experiment with efficient differential power analysis (DPA) techniques from the paper "Behind the Scene of Side Channel Attacks" by Victor Lomné, Emmanuel Prouff, and Thomas Roche (https://eprint.iacr.org/2013/794). 4 | 5 | To clone this repo with the included example traces you will need [Git-LFS](https://git-lfs.github.com). Without Git-LFS, only pointers to traces will be cloned. 6 | 7 | ## Why 8 | The toolbox was designed with the following in mind: 9 | * state-of-the-art DPA techniques 10 | * performance 11 | * visualization of metrics for security evaluations purpose (and not just attack) 12 | * simplicity and flexibility through use of a language suitable for scientific computing 13 | 14 | In terms of these points, Pysca (still) outperforms some commercial tooling. Pysca is nowadays mostly superseded by https://github.com/Riscure/Jlsca. 15 | 16 | ## What 17 | Pysca implements: 18 | * non-profiled linear-regression analysis (LRA) with configurable basis functions 19 | * classical correlation power analysis (CPA) 20 | * significant speed-up of the above by conditional averaging 21 | * targets: AES (S-box out) and DES (round in XOR round out, round out, S-box out) 22 | * visualization of results 23 | 24 | ## How 25 | 26 | For usage basics refer to the [HOWTO](howto/HOWTO.md). 27 | 28 | For a deeper dive into [leakage modelling using linear regression](https://github.com/ikizhvatov/leakage-modelling-tutorial), clone the tutorial into the subfolder: 29 | 30 | git clone https://github.com/ikizhvatov/leakage-modelling-tutorial.git 31 | 32 | ## Details 33 | Pysca works on traces stored in npz (numpy zipped) format. Example tracesets are included in the repo using git-lfs. The conversion script from Riscure Inspector trs format is included. The trs reader was originally implemented by Erik van den Brink. 34 | 35 | Under the hood, the most interesting technical tricks in pysca are perhaps: 36 | * fast computation of correlation (see https://github.com/ikizhvatov/efficient-columnwise-correlation for a dedicated study) 37 | * conditional averaging implementation for DES (because of all the bit permutations, it requires splitting the leakage function into two stages) 38 | 39 | Author: Ilya Kizhvatov
40 | Version: 1.0, 2017-05-14 41 | -------------------------------------------------------------------------------- /Trace.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Authors: Erik van den Brink, Ilya Kizhvatov, Pol van Aubel 4 | Version: 1.0, 2017-05-14 5 | 6 | Open an INS TraceSet 7 | 8 | Versions of this file: 9 | 10 | v0.6 28-05-2017 (Pol) 11 | * performance greatly improved by using np.fromfile 12 | 13 | v0.55 05-12-2014 (yet unidentified author at Riscure) 14 | * Added writing trace fileSize 15 | 16 | v0.5 12-11-2013 (Ilya) 17 | * Fixed the format typo; a bit of cleanup 18 | 19 | v0.4 12-11-2013 (Ilya) 20 | * Fixed the sample reading bug in general 21 | 22 | v0.3 11-09-2013 (Erik) 23 | * Fixed sample reading bug in getTrace() as pointed out by Ilya 24 | 25 | v0.2 5-3-2013 (Erik) 26 | * Fixed __iter__ method and removed the no longer necesarry getTraces() method 27 | * Moved header constants inside TraceSet class 28 | * Removed debug code 29 | ''' 30 | 31 | from __future__ import print_function 32 | 33 | import sys 34 | import struct 35 | import numpy as np 36 | 37 | class Trace(): 38 | def __init__(self, title, data, samples): 39 | self._title = title 40 | self._data = data 41 | self._samples = samples 42 | 43 | class TraceSet(): 44 | #header definitions 45 | NumberOfTraces = 0x41 46 | NumberOfSamplesPerTrace = 0x42 47 | SampleCoding = 0x43 48 | DataSpace = 0x44 49 | TitleSpace = 0x45 50 | GlobalTitle = 0x46 51 | Description = 0x47 52 | XLabel = 0x49 53 | YLabel = 0x4a 54 | XScale = 0x4b 55 | YScale = 0x4c 56 | ScopeRange = 0x55 57 | ChannelCoupling = 0x56 58 | Offset = 0x57 59 | ScopeID = 0x59 60 | TraceBlock = 0x5F #Trace block, a flat memory space 61 | 62 | # enum for sampe coding 63 | CodingByte = 0x01 64 | CodingShort = 0x02 65 | CodingInt = 0x04 66 | CodingFloat = 0x14 67 | 68 | def __init__(self): 69 | self._handle = None 70 | self._traceBlockOffset = None 71 | self._numberOfTraces = None 72 | self._numberOfSamplesPerTrace = None 73 | self._sampleCoding = None 74 | self._npSampleCoding = None 75 | self._sampleCodingByteSize = None 76 | self._titleSpace = 0 77 | self._dataSpace = 0 78 | self._yscale = 1 79 | self._fileName = None 80 | 81 | #properties 82 | self._sampleSpace = None 83 | self._traceSpace = None 84 | self._traceBlockSpace = 0 85 | 86 | self._iterIndex = 0 87 | 88 | def __iter__(self): 89 | for i in range(self._numberOfTraces): 90 | yield self.getTrace(i) 91 | 92 | def _readUINT8(self): 93 | return struct.unpack("B",self._handle.read(1))[0]; 94 | 95 | def _readUINT16(self): 96 | return struct.unpack("H",self._handle.read(2))[0]; 97 | 98 | def _readUINT32(self): 99 | return struct.unpack("I",self._handle.read(4))[0]; 100 | 101 | def _readFloat32(self): 102 | return struct.unpack("f",self._handle.read(4))[0]; 103 | 104 | def _writeUINT8(self, val): 105 | #print("UINT8: %02x"%(val&0xFF)) 106 | return self._handle.write(struct.pack("B",(val&0xFF))); 107 | 108 | def _writeUINT16(self, val): 109 | #print("UINT16: %04x"%(val&0xFFFF)) 110 | return self._handle.write(struct.pack("H",(val&0xFFFF))); 111 | 112 | def _writeUINT32(self, val): 113 | #print("UINT32: %08x"%(val&0xFFFFFFFF)) 114 | return self._handle.write(struct.pack("I",(val&0xFFFFFFFF))); 115 | 116 | def _writeTitleSpace(self, val): 117 | self._writeUINT8(self.TitleSpace) 118 | self._writeUINT8(1) 119 | self._writeUINT8(val) 120 | self._titleSpace = val 121 | 122 | def _writeNumberOfTraces(self, val): 123 | self._writeUINT8(self.NumberOfTraces) 124 | self._writeUINT8(4) 125 | self._writeUINT32(val) 126 | self._numberOfTraces = val 127 | 128 | def _updateNumberOfTraces(self, val): 129 | self.findTag(self.NumberOfTraces) 130 | self._writeUINT8(self.NumberOfTraces) 131 | self._writeUINT8(4) 132 | self._writeUINT32(val) 133 | self._numberOfTraces = val 134 | 135 | def _writeDataSpace(self, val): 136 | self._writeUINT8(self.DataSpace) 137 | self._writeUINT8(2) 138 | self._writeUINT16(val) 139 | self._dataSpace = val 140 | 141 | def _writeNumberOfSamplesPerTrace(self, val): 142 | self._writeUINT8(self.NumberOfSamplesPerTrace) 143 | self._writeUINT8(4) 144 | self._writeUINT32(val) 145 | self._numberOfSamplesPerTrace = val 146 | 147 | def _writeSampleCoding(self, val): 148 | self._writeUINT8(self.SampleCoding) 149 | self._writeUINT8(1) 150 | self._writeUINT8(val) 151 | self._sampleCoding = val 152 | 153 | def _writeTraceBlock(self): 154 | self._writeUINT8(self.TraceBlock) 155 | self._writeUINT8(0) 156 | 157 | def open(self, fileName): 158 | self._handle = open(fileName,'rb') 159 | f = self._handle 160 | f.seek(0,2) 161 | 162 | fileSize = f.tell() 163 | f.seek(0) 164 | offset = 0 165 | 166 | while (offset < fileSize - self._traceBlockSpace): 167 | tag = ord(f.read(1)) 168 | length = ord(f.read(1)) 169 | addLen = 0 170 | 171 | if ((length & 0x80) != 0): #length is encoded in more then 1 byte 172 | addLen = length & 0x7F #how many byte the length is actually encoded in. 173 | length = 0 174 | for i in range(addLen): 175 | length = length + (ord(f.read(1)) << (i * 8)) 176 | 177 | if tag == self.TraceBlock: 178 | self._sampleSpace = self._numberOfSamplesPerTrace * self._sampleCodingByteSize 179 | self._traceSpace = self._sampleSpace + self._dataSpace + self._titleSpace 180 | self._traceBlockSpace = self._numberOfTraces * self._traceSpace 181 | 182 | self._traceBlockOffset = f.tell() #get current pos 183 | f.seek(self._traceBlockOffset + self._traceBlockSpace) # XXX: why this? 184 | elif tag == self.TitleSpace: 185 | if length != 1: 186 | raise ValueError("Incorrect length for TitleSpace header field") 187 | self._titleSpace = self._readUINT8() 188 | elif tag == self.NumberOfTraces: 189 | if length != 4: 190 | raise ValueError("Incorrect length for NumberOfTraces header field") 191 | self._numberOfTraces = self._readUINT32() 192 | elif tag == self.DataSpace: 193 | if length != 2: 194 | raise ValueError("Incorrect length for DataSpace header field") 195 | self._dataSpace = self._readUINT16() 196 | elif tag == self.NumberOfSamplesPerTrace: 197 | if length != 4: 198 | raise ValueError("Incorrect length for NumberOfSamplesPerTrace header field") 199 | self._numberOfSamplesPerTrace = self._readUINT32() 200 | elif tag == self.YScale: 201 | if length != 4: 202 | raise ValueError("Incorrect length for YScale header field") 203 | self._yscale = self._readFloat32() 204 | elif tag == self.SampleCoding: 205 | if length != 1: 206 | raise ValueError("Incorrect length for SampleCoding header field") 207 | self._sampleCoding = self._readUINT8() 208 | #compensate for float sample coding tag 209 | if self._sampleCoding == self.CodingFloat: #float 210 | self._sampleCodingByteSize = 4 211 | self._npSampleCoding = "float32" 212 | else: 213 | self._npSampleCoding = "int" + str(self._sampleCoding * 8) 214 | self._sampleCodingByteSize = self._sampleCoding 215 | else: 216 | print("Unhandled tag: %x len: %d" % (tag, length), file=sys.stderr) # TODO: support other optional tags 217 | f.read(length) 218 | 219 | offset = offset + 2 + addLen + length 220 | 221 | def findTag(self,searchTag): 222 | if (self._handle == None): 223 | return 0 224 | f = self._handle 225 | f.seek(0,2) 226 | fileSize = f.tell() 227 | f.seek(0) 228 | offset = 0 229 | 230 | while (offset < fileSize - self._traceBlockSpace): 231 | tag = ord(f.read(1)) 232 | length = ord(f.read(1)) 233 | addLen = 0 234 | 235 | if ((length & 0x80) != 0): #length is encoded in more then 1 byte 236 | addLen = length & 0x7F #how many byte the length is actually encoded in. 237 | length = 0 238 | for i in range(addLen): 239 | length = length + (ord(f.read(1)) << (i * 8)) 240 | if (tag == searchTag): 241 | f.seek(offset) 242 | return 1 243 | f.read(length) 244 | offset = offset + 2 + addLen + length 245 | return 0 246 | 247 | def new(self, fileName, titleSpace, sampleCoding, dataSpace, numberOfSamples): 248 | self._fileName = fileName 249 | self._handle = open(fileName,'wb') 250 | self._handle.close() 251 | self._handle = open(fileName,'r+b') 252 | 253 | self._writeTitleSpace(titleSpace) 254 | self._writeSampleCoding(sampleCoding) 255 | if self._sampleCoding == self.CodingFloat: #float 256 | self._sampleCodingByteSize = 4 257 | self._npSampleCoding = "float32" 258 | else: 259 | self._npSampleCoding = "int" + str(self._sampleCoding * 8) 260 | self._sampleCodingByteSize = self._sampleCoding 261 | self._writeDataSpace(dataSpace) 262 | self._writeNumberOfSamplesPerTrace(numberOfSamples) 263 | self._writeNumberOfTraces(0) 264 | self._sampleSpace = self._numberOfSamplesPerTrace * self._sampleCodingByteSize 265 | self._traceSpace = self._sampleSpace + self._dataSpace + self._titleSpace 266 | self._traceBlockSpace = self._numberOfTraces * self._traceSpace 267 | self._writeTraceBlock() 268 | self._traceBlockOffset = self._handle.tell() #get current pos 269 | 270 | def getTrace(self, traceIndex): 271 | if (self._handle == None): 272 | return None 273 | f = self._handle 274 | f.seek(self._traceBlockOffset + traceIndex * self._traceSpace) 275 | 276 | title = f.read(self._titleSpace) 277 | data = np.fromfile(f, dtype='uint8', count=self._dataSpace) 278 | samples = np.fromfile(f, dtype=self._npSampleCoding, count=self._numberOfSamplesPerTrace) 279 | 280 | return Trace(title, data, samples) 281 | 282 | def addTrace(self, trace): 283 | if (self._handle == None): 284 | return 285 | f = self._handle 286 | f.seek(0,2) 287 | 288 | # we only accept exact same size inputs 289 | if (len(trace._title) != self._titleSpace): 290 | raise ValueError("Wrong title size! %08x/%08x"%(len(trace._title),self._titleSpace)) 291 | f.write(trace._title) 292 | if (len(trace._data) != self._dataSpace): 293 | raise ValueError("Wrong data size!") 294 | trace._data.tofile(f) 295 | trace._samples.tofile(f) 296 | self._numberOfTraces+=1 297 | # update the trace count 298 | self._updateNumberOfTraces(self._numberOfTraces) 299 | # go to the end again 300 | f.seek(0,2) 301 | 302 | def close(self): 303 | self._handle.close() 304 | -------------------------------------------------------------------------------- /aes.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # 3 | # aes.py: implements AES - Advanced Encryption Standard 4 | # from the SlowAES project, http://code.google.com/p/slowaes/ 5 | # 6 | # Copyright (c) 2008 Josh Davis ( http://www.josh-davis.org ), 7 | # Alex Martelli ( http://www.aleax.it ) 8 | # 9 | # Ported from C code written by Laurent Haan ( http://www.progressive-coding.com ) 10 | # 11 | # Licensed under the Apache License, Version 2.0 12 | # http://www.apache.org/licenses/ 13 | # 14 | import os 15 | import sys 16 | import math 17 | 18 | class AES(object): 19 | '''AES funtions for a single block 20 | ''' 21 | # Very annoying code: all is for an object, but no state is kept! 22 | # Should just be plain functions in a AES modlule. 23 | 24 | # valid key sizes 25 | keySize = dict(SIZE_128=16, SIZE_192=24, SIZE_256=32) 26 | 27 | # Rijndael S-box 28 | sbox = [0x63, 0x7c, 0x77, 0x7b, 0xf2, 0x6b, 0x6f, 0xc5, 0x30, 0x01, 0x67, 29 | 0x2b, 0xfe, 0xd7, 0xab, 0x76, 0xca, 0x82, 0xc9, 0x7d, 0xfa, 0x59, 30 | 0x47, 0xf0, 0xad, 0xd4, 0xa2, 0xaf, 0x9c, 0xa4, 0x72, 0xc0, 0xb7, 31 | 0xfd, 0x93, 0x26, 0x36, 0x3f, 0xf7, 0xcc, 0x34, 0xa5, 0xe5, 0xf1, 32 | 0x71, 0xd8, 0x31, 0x15, 0x04, 0xc7, 0x23, 0xc3, 0x18, 0x96, 0x05, 33 | 0x9a, 0x07, 0x12, 0x80, 0xe2, 0xeb, 0x27, 0xb2, 0x75, 0x09, 0x83, 34 | 0x2c, 0x1a, 0x1b, 0x6e, 0x5a, 0xa0, 0x52, 0x3b, 0xd6, 0xb3, 0x29, 35 | 0xe3, 0x2f, 0x84, 0x53, 0xd1, 0x00, 0xed, 0x20, 0xfc, 0xb1, 0x5b, 36 | 0x6a, 0xcb, 0xbe, 0x39, 0x4a, 0x4c, 0x58, 0xcf, 0xd0, 0xef, 0xaa, 37 | 0xfb, 0x43, 0x4d, 0x33, 0x85, 0x45, 0xf9, 0x02, 0x7f, 0x50, 0x3c, 38 | 0x9f, 0xa8, 0x51, 0xa3, 0x40, 0x8f, 0x92, 0x9d, 0x38, 0xf5, 0xbc, 39 | 0xb6, 0xda, 0x21, 0x10, 0xff, 0xf3, 0xd2, 0xcd, 0x0c, 0x13, 0xec, 40 | 0x5f, 0x97, 0x44, 0x17, 0xc4, 0xa7, 0x7e, 0x3d, 0x64, 0x5d, 0x19, 41 | 0x73, 0x60, 0x81, 0x4f, 0xdc, 0x22, 0x2a, 0x90, 0x88, 0x46, 0xee, 42 | 0xb8, 0x14, 0xde, 0x5e, 0x0b, 0xdb, 0xe0, 0x32, 0x3a, 0x0a, 0x49, 43 | 0x06, 0x24, 0x5c, 0xc2, 0xd3, 0xac, 0x62, 0x91, 0x95, 0xe4, 0x79, 44 | 0xe7, 0xc8, 0x37, 0x6d, 0x8d, 0xd5, 0x4e, 0xa9, 0x6c, 0x56, 0xf4, 45 | 0xea, 0x65, 0x7a, 0xae, 0x08, 0xba, 0x78, 0x25, 0x2e, 0x1c, 0xa6, 46 | 0xb4, 0xc6, 0xe8, 0xdd, 0x74, 0x1f, 0x4b, 0xbd, 0x8b, 0x8a, 0x70, 47 | 0x3e, 0xb5, 0x66, 0x48, 0x03, 0xf6, 0x0e, 0x61, 0x35, 0x57, 0xb9, 48 | 0x86, 0xc1, 0x1d, 0x9e, 0xe1, 0xf8, 0x98, 0x11, 0x69, 0xd9, 0x8e, 49 | 0x94, 0x9b, 0x1e, 0x87, 0xe9, 0xce, 0x55, 0x28, 0xdf, 0x8c, 0xa1, 50 | 0x89, 0x0d, 0xbf, 0xe6, 0x42, 0x68, 0x41, 0x99, 0x2d, 0x0f, 0xb0, 51 | 0x54, 0xbb, 0x16] 52 | 53 | # Rijndael Inverted S-box 54 | rsbox = [0x52, 0x09, 0x6a, 0xd5, 0x30, 0x36, 0xa5, 0x38, 0xbf, 0x40, 0xa3, 55 | 0x9e, 0x81, 0xf3, 0xd7, 0xfb , 0x7c, 0xe3, 0x39, 0x82, 0x9b, 0x2f, 56 | 0xff, 0x87, 0x34, 0x8e, 0x43, 0x44, 0xc4, 0xde, 0xe9, 0xcb , 0x54, 57 | 0x7b, 0x94, 0x32, 0xa6, 0xc2, 0x23, 0x3d, 0xee, 0x4c, 0x95, 0x0b, 58 | 0x42, 0xfa, 0xc3, 0x4e , 0x08, 0x2e, 0xa1, 0x66, 0x28, 0xd9, 0x24, 59 | 0xb2, 0x76, 0x5b, 0xa2, 0x49, 0x6d, 0x8b, 0xd1, 0x25 , 0x72, 0xf8, 60 | 0xf6, 0x64, 0x86, 0x68, 0x98, 0x16, 0xd4, 0xa4, 0x5c, 0xcc, 0x5d, 61 | 0x65, 0xb6, 0x92 , 0x6c, 0x70, 0x48, 0x50, 0xfd, 0xed, 0xb9, 0xda, 62 | 0x5e, 0x15, 0x46, 0x57, 0xa7, 0x8d, 0x9d, 0x84 , 0x90, 0xd8, 0xab, 63 | 0x00, 0x8c, 0xbc, 0xd3, 0x0a, 0xf7, 0xe4, 0x58, 0x05, 0xb8, 0xb3, 64 | 0x45, 0x06 , 0xd0, 0x2c, 0x1e, 0x8f, 0xca, 0x3f, 0x0f, 0x02, 0xc1, 65 | 0xaf, 0xbd, 0x03, 0x01, 0x13, 0x8a, 0x6b , 0x3a, 0x91, 0x11, 0x41, 66 | 0x4f, 0x67, 0xdc, 0xea, 0x97, 0xf2, 0xcf, 0xce, 0xf0, 0xb4, 0xe6, 67 | 0x73 , 0x96, 0xac, 0x74, 0x22, 0xe7, 0xad, 0x35, 0x85, 0xe2, 0xf9, 68 | 0x37, 0xe8, 0x1c, 0x75, 0xdf, 0x6e , 0x47, 0xf1, 0x1a, 0x71, 0x1d, 69 | 0x29, 0xc5, 0x89, 0x6f, 0xb7, 0x62, 0x0e, 0xaa, 0x18, 0xbe, 0x1b , 70 | 0xfc, 0x56, 0x3e, 0x4b, 0xc6, 0xd2, 0x79, 0x20, 0x9a, 0xdb, 0xc0, 71 | 0xfe, 0x78, 0xcd, 0x5a, 0xf4 , 0x1f, 0xdd, 0xa8, 0x33, 0x88, 0x07, 72 | 0xc7, 0x31, 0xb1, 0x12, 0x10, 0x59, 0x27, 0x80, 0xec, 0x5f , 0x60, 73 | 0x51, 0x7f, 0xa9, 0x19, 0xb5, 0x4a, 0x0d, 0x2d, 0xe5, 0x7a, 0x9f, 74 | 0x93, 0xc9, 0x9c, 0xef , 0xa0, 0xe0, 0x3b, 0x4d, 0xae, 0x2a, 0xf5, 75 | 0xb0, 0xc8, 0xeb, 0xbb, 0x3c, 0x83, 0x53, 0x99, 0x61 , 0x17, 0x2b, 76 | 0x04, 0x7e, 0xba, 0x77, 0xd6, 0x26, 0xe1, 0x69, 0x14, 0x63, 0x55, 77 | 0x21, 0x0c, 0x7d] 78 | 79 | def getSBoxValue(self,num): 80 | """Retrieves a given S-Box Value""" 81 | return self.sbox[num] 82 | 83 | def getSBoxInvert(self,num): 84 | """Retrieves a given Inverted S-Box Value""" 85 | return self.rsbox[num] 86 | 87 | def rotate(self, word): 88 | """ Rijndael's key schedule rotate operation. 89 | 90 | Rotate a word eight bits to the left: eg, rotate(1d2c3a4f) == 2c3a4f1d 91 | Word is an char list of size 4 (32 bits overall). 92 | """ 93 | return word[1:] + word[:1] 94 | 95 | # Rijndael Rcon 96 | Rcon = [0x8d, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x1b, 0x36, 97 | 0x6c, 0xd8, 0xab, 0x4d, 0x9a, 0x2f, 0x5e, 0xbc, 0x63, 0xc6, 0x97, 98 | 0x35, 0x6a, 0xd4, 0xb3, 0x7d, 0xfa, 0xef, 0xc5, 0x91, 0x39, 0x72, 99 | 0xe4, 0xd3, 0xbd, 0x61, 0xc2, 0x9f, 0x25, 0x4a, 0x94, 0x33, 0x66, 100 | 0xcc, 0x83, 0x1d, 0x3a, 0x74, 0xe8, 0xcb, 0x8d, 0x01, 0x02, 0x04, 101 | 0x08, 0x10, 0x20, 0x40, 0x80, 0x1b, 0x36, 0x6c, 0xd8, 0xab, 0x4d, 102 | 0x9a, 0x2f, 0x5e, 0xbc, 0x63, 0xc6, 0x97, 0x35, 0x6a, 0xd4, 0xb3, 103 | 0x7d, 0xfa, 0xef, 0xc5, 0x91, 0x39, 0x72, 0xe4, 0xd3, 0xbd, 0x61, 104 | 0xc2, 0x9f, 0x25, 0x4a, 0x94, 0x33, 0x66, 0xcc, 0x83, 0x1d, 0x3a, 105 | 0x74, 0xe8, 0xcb, 0x8d, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 106 | 0x80, 0x1b, 0x36, 0x6c, 0xd8, 0xab, 0x4d, 0x9a, 0x2f, 0x5e, 0xbc, 107 | 0x63, 0xc6, 0x97, 0x35, 0x6a, 0xd4, 0xb3, 0x7d, 0xfa, 0xef, 0xc5, 108 | 0x91, 0x39, 0x72, 0xe4, 0xd3, 0xbd, 0x61, 0xc2, 0x9f, 0x25, 0x4a, 109 | 0x94, 0x33, 0x66, 0xcc, 0x83, 0x1d, 0x3a, 0x74, 0xe8, 0xcb, 0x8d, 110 | 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x1b, 0x36, 0x6c, 111 | 0xd8, 0xab, 0x4d, 0x9a, 0x2f, 0x5e, 0xbc, 0x63, 0xc6, 0x97, 0x35, 112 | 0x6a, 0xd4, 0xb3, 0x7d, 0xfa, 0xef, 0xc5, 0x91, 0x39, 0x72, 0xe4, 113 | 0xd3, 0xbd, 0x61, 0xc2, 0x9f, 0x25, 0x4a, 0x94, 0x33, 0x66, 0xcc, 114 | 0x83, 0x1d, 0x3a, 0x74, 0xe8, 0xcb, 0x8d, 0x01, 0x02, 0x04, 0x08, 115 | 0x10, 0x20, 0x40, 0x80, 0x1b, 0x36, 0x6c, 0xd8, 0xab, 0x4d, 0x9a, 116 | 0x2f, 0x5e, 0xbc, 0x63, 0xc6, 0x97, 0x35, 0x6a, 0xd4, 0xb3, 0x7d, 117 | 0xfa, 0xef, 0xc5, 0x91, 0x39, 0x72, 0xe4, 0xd3, 0xbd, 0x61, 0xc2, 118 | 0x9f, 0x25, 0x4a, 0x94, 0x33, 0x66, 0xcc, 0x83, 0x1d, 0x3a, 0x74, 119 | 0xe8, 0xcb ] 120 | 121 | def getRconValue(self, num): 122 | """Retrieves a given Rcon Value""" 123 | return self.Rcon[num] 124 | 125 | def core(self, word, iteration): 126 | """Key schedule core.""" 127 | # rotate the 32-bit word 8 bits to the left 128 | word = self.rotate(word) 129 | # apply S-Box substitution on all 4 parts of the 32-bit word 130 | for i in range(4): 131 | word[i] = self.getSBoxValue(word[i]) 132 | # XOR the output of the rcon operation with i to the first part 133 | # (leftmost) only 134 | word[0] = word[0] ^ self.getRconValue(iteration) 135 | return word 136 | 137 | def expandKey(self, key, size, expandedKeySize): 138 | """Rijndael's key expansion. 139 | 140 | Expands an 128,192,256 key into an 176,208,240 bytes key 141 | 142 | expandedKey is a char list of large enough size, 143 | key is the non-expanded key. 144 | """ 145 | # current expanded keySize, in bytes 146 | currentSize = 0 147 | rconIteration = 1 148 | expandedKey = [0] * expandedKeySize 149 | 150 | # set the 16, 24, 32 bytes of the expanded key to the input key 151 | for j in range(size): 152 | expandedKey[j] = key[j] 153 | currentSize += size 154 | 155 | while currentSize < expandedKeySize: 156 | # assign the previous 4 bytes to the temporary value t 157 | t = expandedKey[currentSize-4:currentSize] 158 | 159 | # every 16,24,32 bytes we apply the core schedule to t 160 | # and increment rconIteration afterwards 161 | if currentSize % size == 0: 162 | t = self.core(t, rconIteration) 163 | rconIteration += 1 164 | # For 256-bit keys, we add an extra sbox to the calculation 165 | if size == self.keySize["SIZE_256"] and ((currentSize % size) == 16): 166 | for l in range(4): t[l] = self.getSBoxValue(t[l]) 167 | 168 | # We XOR t with the four-byte block 16,24,32 bytes before the new 169 | # expanded key. This becomes the next four bytes in the expanded 170 | # key. 171 | for m in range(4): 172 | expandedKey[currentSize] = expandedKey[currentSize - size] ^ \ 173 | t[m] 174 | currentSize += 1 175 | 176 | return expandedKey 177 | 178 | def addRoundKey(self, state, roundKey): 179 | """Adds (XORs) the round key to the state.""" 180 | for i in range(16): 181 | state[i] ^= roundKey[i] 182 | return state 183 | 184 | def createRoundKey(self, expandedKey, roundKeyPointer): 185 | """Create a round key. 186 | Creates a round key from the given expanded key and the 187 | position within the expanded key. 188 | """ 189 | roundKey = [0] * 16 190 | for i in range(4): 191 | for j in range(4): 192 | roundKey[j*4+i] = expandedKey[roundKeyPointer + i*4 + j] 193 | return roundKey 194 | 195 | def galois_multiplication(self, a, b): 196 | """Galois multiplication of 8 bit characters a and b.""" 197 | p = 0 198 | for counter in range(8): 199 | if b & 1: p ^= a 200 | hi_bit_set = a & 0x80 201 | a <<= 1 202 | # keep a 8 bit 203 | a &= 0xFF 204 | if hi_bit_set: 205 | a ^= 0x1b 206 | b >>= 1 207 | return p 208 | 209 | # 210 | # substitute all the values from the state with the value in the SBox 211 | # using the state value as index for the SBox 212 | # 213 | def subBytes(self, state, isInv): 214 | if isInv: getter = self.getSBoxInvert 215 | else: getter = self.getSBoxValue 216 | for i in range(16): state[i] = getter(state[i]) 217 | return state 218 | 219 | # iterate over the 4 rows and call shiftRow() with that row 220 | def shiftRows(self, state, isInv): 221 | for i in range(4): 222 | state = self.shiftRow(state, i*4, i, isInv) 223 | return state 224 | 225 | # each iteration shifts the row to the left by 1 226 | def shiftRow(self, state, statePointer, nbr, isInv): 227 | for i in range(nbr): 228 | if isInv: 229 | state[statePointer:statePointer+4] = \ 230 | state[statePointer+3:statePointer+4] + \ 231 | state[statePointer:statePointer+3] 232 | else: 233 | state[statePointer:statePointer+4] = \ 234 | state[statePointer+1:statePointer+4] + \ 235 | state[statePointer:statePointer+1] 236 | return state 237 | 238 | # galois multiplication of the 4x4 matrix 239 | def mixColumns(self, state, isInv): 240 | # iterate over the 4 columns 241 | for i in range(4): 242 | # construct one column by slicing over the 4 rows 243 | column = state[i:i+16:4] 244 | # apply the mixColumn on one column 245 | column = self.mixColumn(column, isInv) 246 | # put the values back into the state 247 | state[i:i+16:4] = column 248 | 249 | return state 250 | 251 | # galois multiplication of 1 column of the 4x4 matrix 252 | def mixColumn(self, column, isInv): 253 | if isInv: mult = [14, 9, 13, 11] 254 | else: mult = [2, 1, 1, 3] 255 | cpy = list(column) 256 | g = self.galois_multiplication 257 | 258 | column[0] = g(cpy[0], mult[0]) ^ g(cpy[3], mult[1]) ^ \ 259 | g(cpy[2], mult[2]) ^ g(cpy[1], mult[3]) 260 | column[1] = g(cpy[1], mult[0]) ^ g(cpy[0], mult[1]) ^ \ 261 | g(cpy[3], mult[2]) ^ g(cpy[2], mult[3]) 262 | column[2] = g(cpy[2], mult[0]) ^ g(cpy[1], mult[1]) ^ \ 263 | g(cpy[0], mult[2]) ^ g(cpy[3], mult[3]) 264 | column[3] = g(cpy[3], mult[0]) ^ g(cpy[2], mult[1]) ^ \ 265 | g(cpy[1], mult[2]) ^ g(cpy[0], mult[3]) 266 | return column 267 | 268 | # applies the 4 operations of the forward round in sequence 269 | def aes_round(self, state, roundKey): 270 | state = self.subBytes(state, False) 271 | state = self.shiftRows(state, False) 272 | state = self.mixColumns(state, False) 273 | state = self.addRoundKey(state, roundKey) 274 | return state 275 | 276 | # applies the 4 operations of the inverse round in sequence 277 | def aes_invRound(self, state, roundKey): 278 | state = self.shiftRows(state, True) 279 | state = self.subBytes(state, True) 280 | state = self.addRoundKey(state, roundKey) 281 | state = self.mixColumns(state, True) 282 | return state 283 | 284 | # Perform the initial operations, the standard round, and the final 285 | # operations of the forward aes, creating a round key for each round 286 | def aes_main(self, state, expandedKey, nbrRounds): 287 | state = self.addRoundKey(state, self.createRoundKey(expandedKey, 0)) 288 | i = 1 289 | while i < nbrRounds: 290 | state = self.aes_round(state, 291 | self.createRoundKey(expandedKey, 16*i)) 292 | i += 1 293 | state = self.subBytes(state, False) 294 | state = self.shiftRows(state, False) 295 | state = self.addRoundKey(state, 296 | self.createRoundKey(expandedKey, 16*nbrRounds)) 297 | return state 298 | 299 | # Perform the initial operations, the standard round, and the final 300 | # operations of the inverse aes, creating a round key for each round 301 | def aes_invMain(self, state, expandedKey, nbrRounds): 302 | state = self.addRoundKey(state, 303 | self.createRoundKey(expandedKey, 16*nbrRounds)) 304 | i = nbrRounds - 1 305 | while i > 0: 306 | state = self.aes_invRound(state, 307 | self.createRoundKey(expandedKey, 16*i)) 308 | i -= 1 309 | state = self.shiftRows(state, True) 310 | state = self.subBytes(state, True) 311 | state = self.addRoundKey(state, self.createRoundKey(expandedKey, 0)) 312 | return state 313 | 314 | # encrypts a 128 bit input block against the given key of size specified 315 | def encrypt(self, iput, key, size): 316 | output = [0] * 16 317 | # the number of rounds 318 | nbrRounds = 0 319 | # the 128 bit block to encode 320 | block = [0] * 16 321 | # set the number of rounds 322 | if size == self.keySize["SIZE_128"]: nbrRounds = 10 323 | elif size == self.keySize["SIZE_192"]: nbrRounds = 12 324 | elif size == self.keySize["SIZE_256"]: nbrRounds = 14 325 | else: return None 326 | 327 | # the expanded keySize 328 | expandedKeySize = 16*(nbrRounds+1) 329 | 330 | # Set the block values, for the block: 331 | # a0,0 a0,1 a0,2 a0,3 332 | # a1,0 a1,1 a1,2 a1,3 333 | # a2,0 a2,1 a2,2 a2,3 334 | # a3,0 a3,1 a3,2 a3,3 335 | # the mapping order is a0,0 a1,0 a2,0 a3,0 a0,1 a1,1 ... a2,3 a3,3 336 | # 337 | # iterate over the columns 338 | for i in range(4): 339 | # iterate over the rows 340 | for j in range(4): 341 | block[(i+(j*4))] = iput[(i*4)+j] 342 | 343 | # expand the key into an 176, 208, 240 bytes key 344 | # the expanded key 345 | expandedKey = self.expandKey(key, size, expandedKeySize) 346 | 347 | # encrypt the block using the expandedKey 348 | block = self.aes_main(block, expandedKey, nbrRounds) 349 | 350 | # unmap the block again into the output 351 | for k in range(4): 352 | # iterate over the rows 353 | for l in range(4): 354 | output[(k*4)+l] = block[(k+(l*4))] 355 | return output 356 | 357 | # decrypts a 128 bit input block against the given key of size specified 358 | def decrypt(self, iput, key, size): 359 | output = [0] * 16 360 | # the number of rounds 361 | nbrRounds = 0 362 | # the 128 bit block to decode 363 | block = [0] * 16 364 | # set the number of rounds 365 | if size == self.keySize["SIZE_128"]: nbrRounds = 10 366 | elif size == self.keySize["SIZE_192"]: nbrRounds = 12 367 | elif size == self.keySize["SIZE_256"]: nbrRounds = 14 368 | else: return None 369 | 370 | # the expanded keySize 371 | expandedKeySize = 16*(nbrRounds+1) 372 | 373 | # Set the block values, for the block: 374 | # a0,0 a0,1 a0,2 a0,3 375 | # a1,0 a1,1 a1,2 a1,3 376 | # a2,0 a2,1 a2,2 a2,3 377 | # a3,0 a3,1 a3,2 a3,3 378 | # the mapping order is a0,0 a1,0 a2,0 a3,0 a0,1 a1,1 ... a2,3 a3,3 379 | 380 | # iterate over the columns 381 | for i in range(4): 382 | # iterate over the rows 383 | for j in range(4): 384 | block[(i+(j*4))] = iput[(i*4)+j] 385 | # expand the key into an 176, 208, 240 bytes key 386 | expandedKey = self.expandKey(key, size, expandedKeySize) 387 | # decrypt the block using the expandedKey 388 | block = self.aes_invMain(block, expandedKey, nbrRounds) 389 | # unmap the block again into the output 390 | for k in range(4): 391 | # iterate over the rows 392 | for l in range(4): 393 | output[(k*4)+l] = block[(k+(l*4))] 394 | return output 395 | 396 | 397 | class AESModeOfOperation(object): 398 | '''Handles AES with plaintext consistingof multiple blocks. 399 | Choice of block encoding modes: OFT, CFB, CBC 400 | ''' 401 | # Very annoying code: all is for an object, but no state is kept! 402 | # Should just be plain functions in an AES_BlockMode module. 403 | aes = AES() 404 | 405 | # structure of supported modes of operation 406 | modeOfOperation = dict(OFB=0, CFB=1, CBC=2) 407 | 408 | # converts a 16 character string into a number array 409 | def convertString(self, string, start, end, mode): 410 | if end - start > 16: end = start + 16 411 | if mode == self.modeOfOperation["CBC"]: ar = [0] * 16 412 | else: ar = [] 413 | 414 | i = start 415 | j = 0 416 | while len(ar) < end - start: 417 | ar.append(0) 418 | while i < end: 419 | ar[j] = ord(string[i]) 420 | j += 1 421 | i += 1 422 | return ar 423 | 424 | # Mode of Operation Encryption 425 | # stringIn - Input String 426 | # mode - mode of type modeOfOperation 427 | # hexKey - a hex key of the bit length size 428 | # size - the bit length of the key 429 | # hexIV - the 128 bit hex Initilization Vector 430 | def encrypt(self, stringIn, mode, key, size, IV): 431 | if len(key) % size: 432 | return None 433 | if len(IV) % 16: 434 | return None 435 | # the AES input/output 436 | plaintext = [] 437 | iput = [0] * 16 438 | output = [] 439 | ciphertext = [0] * 16 440 | # the output cipher string 441 | cipherOut = [] 442 | # char firstRound 443 | firstRound = True 444 | if stringIn != None: 445 | for j in range(int(math.ceil(float(len(stringIn))/16))): 446 | start = j*16 447 | end = j*16+16 448 | if end > len(stringIn): 449 | end = len(stringIn) 450 | plaintext = self.convertString(stringIn, start, end, mode) 451 | # print 'PT@%s:%s' % (j, plaintext) 452 | if mode == self.modeOfOperation["CFB"]: 453 | if firstRound: 454 | output = self.aes.encrypt(IV, key, size) 455 | firstRound = False 456 | else: 457 | output = self.aes.encrypt(iput, key, size) 458 | for i in range(16): 459 | if len(plaintext)-1 < i: 460 | ciphertext[i] = 0 ^ output[i] 461 | elif len(output)-1 < i: 462 | ciphertext[i] = plaintext[i] ^ 0 463 | elif len(plaintext)-1 < i and len(output) < i: 464 | ciphertext[i] = 0 ^ 0 465 | else: 466 | ciphertext[i] = plaintext[i] ^ output[i] 467 | for k in range(end-start): 468 | cipherOut.append(ciphertext[k]) 469 | iput = ciphertext 470 | elif mode == self.modeOfOperation["OFB"]: 471 | if firstRound: 472 | output = self.aes.encrypt(IV, key, size) 473 | firstRound = False 474 | else: 475 | output = self.aes.encrypt(iput, key, size) 476 | for i in range(16): 477 | if len(plaintext)-1 < i: 478 | ciphertext[i] = 0 ^ output[i] 479 | elif len(output)-1 < i: 480 | ciphertext[i] = plaintext[i] ^ 0 481 | elif len(plaintext)-1 < i and len(output) < i: 482 | ciphertext[i] = 0 ^ 0 483 | else: 484 | ciphertext[i] = plaintext[i] ^ output[i] 485 | for k in range(end-start): 486 | cipherOut.append(ciphertext[k]) 487 | iput = output 488 | elif mode == self.modeOfOperation["CBC"]: 489 | for i in range(16): 490 | if firstRound: 491 | iput[i] = plaintext[i] ^ IV[i] 492 | else: 493 | iput[i] = plaintext[i] ^ ciphertext[i] 494 | # print 'IP@%s:%s' % (j, iput) 495 | firstRound = False 496 | ciphertext = self.aes.encrypt(iput, key, size) 497 | # always 16 bytes because of the padding for CBC 498 | for k in range(16): 499 | cipherOut.append(ciphertext[k]) 500 | return mode, len(stringIn), cipherOut 501 | 502 | # Mode of Operation Decryption 503 | # cipherIn - Encrypted String 504 | # originalsize - The unencrypted string length - required for CBC 505 | # mode - mode of type modeOfOperation 506 | # key - a number array of the bit length size 507 | # size - the bit length of the key 508 | # IV - the 128 bit number array Initilization Vector 509 | def decrypt(self, cipherIn, originalsize, mode, key, size, IV): 510 | # cipherIn = unescCtrlChars(cipherIn) 511 | if len(key) % size: 512 | return None 513 | if len(IV) % 16: 514 | return None 515 | # the AES input/output 516 | ciphertext = [] 517 | iput = [] 518 | output = [] 519 | plaintext = [0] * 16 520 | # the output plain text character list 521 | chrOut = [] 522 | # char firstRound 523 | firstRound = True 524 | if cipherIn != None: 525 | for j in range(int(math.ceil(float(len(cipherIn))/16))): 526 | start = j*16 527 | end = j*16+16 528 | if j*16+16 > len(cipherIn): 529 | end = len(cipherIn) 530 | ciphertext = cipherIn[start:end] 531 | if mode == self.modeOfOperation["CFB"]: 532 | if firstRound: 533 | output = self.aes.encrypt(IV, key, size) 534 | firstRound = False 535 | else: 536 | output = self.aes.encrypt(iput, key, size) 537 | for i in range(16): 538 | if len(output)-1 < i: 539 | plaintext[i] = 0 ^ ciphertext[i] 540 | elif len(ciphertext)-1 < i: 541 | plaintext[i] = output[i] ^ 0 542 | elif len(output)-1 < i and len(ciphertext) < i: 543 | plaintext[i] = 0 ^ 0 544 | else: 545 | plaintext[i] = output[i] ^ ciphertext[i] 546 | for k in range(end-start): 547 | chrOut.append(chr(plaintext[k])) 548 | iput = ciphertext 549 | elif mode == self.modeOfOperation["OFB"]: 550 | if firstRound: 551 | output = self.aes.encrypt(IV, key, size) 552 | firstRound = False 553 | else: 554 | output = self.aes.encrypt(iput, key, size) 555 | for i in range(16): 556 | if len(output)-1 < i: 557 | plaintext[i] = 0 ^ ciphertext[i] 558 | elif len(ciphertext)-1 < i: 559 | plaintext[i] = output[i] ^ 0 560 | elif len(output)-1 < i and len(ciphertext) < i: 561 | plaintext[i] = 0 ^ 0 562 | else: 563 | plaintext[i] = output[i] ^ ciphertext[i] 564 | for k in range(end-start): 565 | chrOut.append(chr(plaintext[k])) 566 | iput = output 567 | elif mode == self.modeOfOperation["CBC"]: 568 | output = self.aes.decrypt(ciphertext, key, size) 569 | for i in range(16): 570 | if firstRound: 571 | plaintext[i] = IV[i] ^ output[i] 572 | else: 573 | plaintext[i] = iput[i] ^ output[i] 574 | firstRound = False 575 | if originalsize is not None and originalsize < end: 576 | for k in range(originalsize-start): 577 | chrOut.append(chr(plaintext[k])) 578 | else: 579 | for k in range(end-start): 580 | chrOut.append(chr(plaintext[k])) 581 | iput = ciphertext 582 | return "".join(chrOut) 583 | 584 | 585 | def append_PKCS7_padding(s): 586 | """return s padded to a multiple of 16-bytes by PKCS7 padding""" 587 | numpads = 16 - (len(s)%16) 588 | return s + numpads*chr(numpads) 589 | 590 | def strip_PKCS7_padding(s): 591 | """return s stripped of PKCS7 padding""" 592 | if len(s)%16 or not s: 593 | raise ValueError("String of len %d can't be PCKS7-padded" % len(s)) 594 | numpads = ord(s[-1]) 595 | if numpads > 16: 596 | raise ValueError("String ending with %r can't be PCKS7-padded" % s[-1]) 597 | return s[:-numpads] 598 | 599 | def encryptData(key, data, mode=AESModeOfOperation.modeOfOperation["CBC"]): 600 | """encrypt `data` using `key` 601 | 602 | `key` should be a string of bytes. 603 | 604 | returned cipher is a string of bytes prepended with the initialization 605 | vector. 606 | 607 | """ 608 | key = map(ord, key) 609 | if mode == AESModeOfOperation.modeOfOperation["CBC"]: 610 | data = append_PKCS7_padding(data) 611 | keysize = len(key) 612 | assert keysize in AES.keySize.values(), 'invalid key size: %s' % keysize 613 | # create a new iv using random data 614 | iv = [ord(i) for i in os.urandom(16)] 615 | moo = AESModeOfOperation() 616 | (mode, length, ciph) = moo.encrypt(data, mode, key, keysize, iv) 617 | # With padding, the original length does not need to be known. It's a bad 618 | # idea to store the original message length. 619 | # prepend the iv. 620 | return ''.join(map(chr, iv)) + ''.join(map(chr, ciph)) 621 | 622 | def decryptData(key, data, mode=AESModeOfOperation.modeOfOperation["CBC"]): 623 | """decrypt `data` using `key` 624 | 625 | `key` should be a string of bytes. 626 | 627 | `data` should have the initialization vector prepended as a string of 628 | ordinal values. 629 | """ 630 | 631 | key = map(ord, key) 632 | keysize = len(key) 633 | assert keysize in AES.keySize.values(), 'invalid key size: %s' % keysize 634 | # iv is first 16 bytes 635 | iv = map(ord, data[:16]) 636 | data = map(ord, data[16:]) 637 | moo = AESModeOfOperation() 638 | decr = moo.decrypt(data, None, mode, key, keysize, iv) 639 | if mode == AESModeOfOperation.modeOfOperation["CBC"]: 640 | decr = strip_PKCS7_padding(decr) 641 | return decr 642 | 643 | def generateRandomKey(keysize): 644 | """Generates a key from random data of length `keysize`. 645 | The returned key is a string of bytes. 646 | """ 647 | if keysize not in (16, 24, 32): 648 | emsg = 'Invalid keysize, %s. Should be one of (16, 24, 32).' 649 | raise ValueError, emsg % keysize 650 | return os.urandom(keysize) 651 | 652 | def testStr(cleartext, keysize=16, modeName = "CBC"): 653 | '''Test with random key, choice of mode.''' 654 | print 'Random key test', 'Mode:', modeName 655 | print 'cleartext:', cleartext 656 | key = generateRandomKey(keysize) 657 | print 'Key:', [ord(x) for x in key] 658 | mode = AESModeOfOperation.modeOfOperation[modeName] 659 | cipher = encryptData(key, cleartext, mode) 660 | print 'Cipher:', [ord(x) for x in cipher] 661 | decr = decryptData(key, cipher, mode) 662 | print 'Decrypted:', decr 663 | 664 | 665 | if __name__ == "__main__": 666 | moo = AESModeOfOperation() 667 | cleartext = "This is a test with several blocks!" 668 | cypherkey = [143,194,34,208,145,203,230,143,177,246,97,206,145,92,255,84] 669 | iv = [103,35,148,239,76,213,47,118,255,222,123,176,106,134,98,92] 670 | mode, orig_len, ciph = moo.encrypt(cleartext, moo.modeOfOperation["CBC"], 671 | cypherkey, moo.aes.keySize["SIZE_128"], iv) 672 | print 'm=%s, ol=%s (%s), ciph=%s' % (mode, orig_len, len(cleartext), ciph) 673 | decr = moo.decrypt(ciph, orig_len, mode, cypherkey, 674 | moo.aes.keySize["SIZE_128"], iv) 675 | print decr 676 | testStr(cleartext, 16, "CBC") 677 | 678 | 679 | 680 | 681 | 682 | -------------------------------------------------------------------------------- /attackaessbox.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | CPA and LRA attacks on the AES S-box 7 | 8 | The code should be self-explanatory (especially if you look into lracpa.py module) 9 | 10 | In the plots: 11 | - red trace is for known correct candidate 12 | - blue trace is for the winning candidate (e.g. the one with maximum peak) 13 | - grey traces are for all other candidates 14 | ''' 15 | 16 | import numpy as np 17 | import matplotlib.pyplot as plt 18 | import time 19 | 20 | from aes import AES # interweb's SlowAES toolbox 21 | from lracpa import * # my LRA-CPA toolbox 22 | from condaveraes import * # incremental conditional averaging 23 | 24 | 25 | ################################################## 26 | ### 0. Configurable parameters 27 | 28 | ## Traceset, number of traces, and S-box to attack 29 | tracesetFilename = "traces/swaes_atmega_power.npz" 30 | sampleRange = (800, 1500) # range of samples to attack, in the format (low, high) 31 | N = 100 # number of traces to attack (less or equal to the amount of traces in the file) 32 | offset = 0 # trace number to start from 33 | evolutionStep = 10 # step for intermediate reports 34 | SboxNum = 2 # S-box to attack, counting from 0 35 | 36 | ## Leakage model 37 | ## (these parameters correspond to function names in lracpa module) 38 | intermediateFunction = sBoxOut # for CPA and LRA 39 | leakageFunction = leakageModelHW # for CPA 40 | basisFunctionsModel = basisModelSingleBits # for LRA 41 | 42 | ## Known key for ranking 43 | knownKeyStr = "2B7E151628AED2A6ABF7158809CF4F3C".decode("hex") # the correct key 44 | encrypt = True # to avoid selective commenting in the following lines below 45 | 46 | if encrypt: # for encryption, the first round key is as is 47 | knownKey = np.array(map(ord, knownKeyStr), dtype="uint8") 48 | else: # for decryption, need to run key expansion 49 | expandedKnownKey = AES().expandKey(map(ord, knownKeyStr), 16, 16 * 11) # this returs a list 50 | knownKey = np.array(expandedKnownKey[176-16:177], dtype="uint8") 51 | 52 | 53 | ################################################## 54 | ### 1. Log the parameters 55 | 56 | print "---\nAttack parameters" 57 | print "Intermediate function :", intermediateFunction.__name__ 58 | print "CPA leakage function :", leakageFunction.__name__ 59 | print "LRA basis functions :", basisFunctionsModel.__name__ 60 | print "Encryption :", encrypt 61 | print "S-box number :", SboxNum 62 | print "Known key : 0x" + knownKeyStr.encode("hex") 63 | print "Known roundkey : 0x%s" % str(bytearray(knownKey)).encode("hex") 64 | 65 | 66 | ################################################# 67 | ### 2. Load samples and data 68 | 69 | # Readout 70 | print "---\nLoading " + tracesetFilename 71 | t0 = time.clock() 72 | npzfile = np.load(tracesetFilename) 73 | data = npzfile['data'][offset:offset + N,SboxNum] # selecting only the required byte 74 | traces = npzfile['traces'][offset:offset + N,sampleRange[0]:sampleRange[1]] 75 | t1 = time.clock() 76 | timeLoad = t1 - t0 77 | 78 | # Log traceset parameters 79 | (numTraces, traceLength) = traces.shape 80 | print "Number of traces loaded :", numTraces 81 | print "Trace length :", traceLength 82 | print "Loading time : %0.2f s" % timeLoad 83 | 84 | 85 | ################################################# 86 | ### 3. LRA and CPA with evolving amount of traces 87 | 88 | print "---\nAttack" 89 | 90 | t0 = time.clock() 91 | 92 | # initialize the incremental averager 93 | CondAver = ConditionalAveragerAesSbox(256, traceLength) 94 | 95 | # allocate arrays for storing key rank evolution 96 | numSteps = int(np.ceil(N / np.double(evolutionStep))) 97 | keyRankEvolutionCPA = np.zeros(numSteps) 98 | keyRankEvolutionLRA = np.zeros(numSteps) 99 | 100 | # the incremental loop 101 | tracesToSkip = 20 # warm-up to avoid numerical problems for small evolution step 102 | for i in range (0, tracesToSkip - 1): 103 | CondAver.addTrace(data[i], traces[i]) 104 | for i in range(tracesToSkip - 1, N): 105 | CondAver.addTrace(data[i], traces[i]) 106 | 107 | if (((i + 1) % evolutionStep == 0) or ((i + 1) == N)): 108 | 109 | (avdata, avtraces) = CondAver.getSnapshot() 110 | 111 | CorrTraces = cpaAES(avdata, avtraces, intermediateFunction, leakageFunction) 112 | R2, coefs = lraAES(avdata, avtraces, intermediateFunction, basisFunctionsModel) 113 | 114 | print "---\nResults after %d traces" % (i + 1) 115 | print "CPA" 116 | CorrPeaks = np.max(np.abs(CorrTraces), axis=1) # global maximization, absolute value! 117 | CpaWinningCandidate = np.argmax(CorrPeaks) 118 | CpaWinningCandidatePeak = np.max(CorrPeaks) 119 | CpaCorrectCandidateRank = np.count_nonzero(CorrPeaks >= CorrPeaks[knownKey[SboxNum]]) 120 | CpaCorrectCandidatePeak = CorrPeaks[knownKey[SboxNum]] 121 | print "Winning candidate: 0x%02x, peak magnitude %f" % (CpaWinningCandidate, CpaWinningCandidatePeak) 122 | print "Correct candidate: 0x%02x, peak magnitude %f, rank %d" % (knownKey[SboxNum], CpaCorrectCandidatePeak, CpaCorrectCandidateRank) 123 | 124 | print "LRA" 125 | R2Peaks = np.max(R2, axis=1) # global maximization 126 | LraWinningCandidate = np.argmax(R2Peaks) 127 | LraWinningCandidatePeak = np.max(R2Peaks) 128 | LraCorrectCandidateRank = np.count_nonzero(R2Peaks >= R2Peaks[knownKey[SboxNum]]) 129 | LraCorrectCandidatePeak = R2Peaks[knownKey[SboxNum]] 130 | print "Winning candidate: 0x%02x, peak magnitude %f" % (LraWinningCandidate, LraWinningCandidatePeak) 131 | print "Correct candidate: 0x%02x, peak magnitude %f, rank %d" % (knownKey[SboxNum], LraCorrectCandidatePeak, LraCorrectCandidateRank) 132 | 133 | stepCount = int(np.floor(i / np.double(evolutionStep))) 134 | keyRankEvolutionCPA[stepCount] = CpaCorrectCandidateRank 135 | keyRankEvolutionLRA[stepCount] = LraCorrectCandidateRank 136 | 137 | t1 = time.clock() 138 | timeAll = t1 - t0 139 | 140 | print "---\nCumulative timing" 141 | print "%0.2f s" % timeAll 142 | 143 | # save the rank evolution for later processing 144 | #np.savez("results/keyRankEvolutionSbox%02d" % SboxNum, kreCPA=keyRankEvolutionCPA, kreLRA=keyRankEvolutionLRA, step=evolutionStep) 145 | 146 | ################################################# 147 | ### 4. Visualize results 148 | 149 | print "---\nPlotting..." 150 | 151 | fig = plt.figure() 152 | 153 | # allocate grid 154 | axCPA = plt.subplot2grid((3, 2), (0, 0)) 155 | axLRA = plt.subplot2grid((3, 2), (1, 0)) 156 | axLRAcoefs = plt.subplot2grid((3, 2), (2, 0)) 157 | axRankEvolution = plt.subplot2grid((2, 2), (0, 1), rowspan = 3) 158 | 159 | # compute trace nubmers for x axis (TODO: move into block 3) 160 | traceNumbers = np.arange(evolutionStep, N + 1, evolutionStep) 161 | 162 | # CPA 163 | axCPA.plot(CorrTraces.T, color = 'grey') 164 | if CpaWinningCandidate != knownKey[SboxNum]: 165 | axCPA.plot(CorrTraces[CpaWinningCandidate, :], 'blue') 166 | axCPA.plot(CorrTraces[knownKey[SboxNum], :], 'r') 167 | axRankEvolution.plot(traceNumbers, keyRankEvolutionCPA, color = 'green') 168 | axCPA.set_xlim([0, traceLength]) 169 | 170 | # LRA 171 | axLRA.plot(R2.T, color = 'grey') 172 | if LraWinningCandidate != knownKey[SboxNum]: 173 | axLRA.plot(R2[LraWinningCandidate, :], 'blue') 174 | axLRA.plot(R2[knownKey[SboxNum], :], 'r') 175 | axRankEvolution.plot(traceNumbers, keyRankEvolutionLRA, color = 'magenta') 176 | axLRA.set_xlim([0, traceLength]) 177 | 178 | # LRA coefs 179 | coefsKnownKey = np.array(coefs[knownKey[SboxNum]]) 180 | axLRAcoefs.pcolormesh(coefsKnownKey[:,:-1].T, cmap="jet") 181 | axLRAcoefs.set_xlim([0, traceLength]) 182 | 183 | # labels 184 | fig.suptitle("CPA and LRA on %d traces" % N) 185 | axCPA.set_ylabel('Correlation') 186 | axLRA.set_ylabel('R2') 187 | axLRAcoefs.set_ylabel('Basis function (bit)') 188 | axLRAcoefs.set_xlabel('Time sample') 189 | axRankEvolution.set_ylabel('Correct key candidate rank') 190 | axRankEvolution.set_xlabel('Number of traces') 191 | axRankEvolution.set_title('Correct key rank evolution (global maximisation)') 192 | 193 | # Limits and tick labels for key rand evolution plot 194 | axRankEvolution.set_xlim([traceNumbers[int(np.ceil(tracesToSkip / np.double(evolutionStep))) - 1], N]) 195 | axRankEvolution.set_ylim([0, 256]) 196 | axRankEvolution.grid(b=True, which='both', color='0.65',linestyle='-') 197 | #axRankEvolution.ticklabel_format(style='sci', axis='x', scilimits=(0,0), useOffset=True) 198 | 199 | # Legend for rank evolution plot 200 | axRankEvolution.legend(['CPA', 'LRA'], loc='upper right') 201 | 202 | plt.show() 203 | -------------------------------------------------------------------------------- /attackdesroundxor.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | CPA and LRA attacks on DES round in XOR out 7 | 8 | The code should be self-explanatory (especially if you look into lracpa.py module) 9 | 10 | In the plots: 11 | - red trace is for known correct candidate 12 | - blue trace is for the winning candidate (e.g. the one with maximum peak) 13 | - grey traces are for all other candidates 14 | ''' 15 | 16 | import numpy as np 17 | import matplotlib.pyplot as plt 18 | import struct 19 | import time 20 | 21 | from desutils import * # my DES utilities 22 | from lracpa import * # my LRA-CPA toolbox 23 | from condaverdes import * # incremental conditional averaging 24 | 25 | 26 | ################################################## 27 | ### 0. Configurable parameters 28 | 29 | ## Traceset, number of traces, and S-box to attack 30 | tracesetFilename = "traces/hwdes_card8_power.npz" 31 | sampleRange = (0, 50) # range of smaples to attack 32 | N = 10000 # number of traces to attack (less or equal to the amount of traces in the file) 33 | offset = 0 # trace number to start from 34 | evolutionStep = 500 # step for intermediate reports 35 | SboxNum = 1 # S-box to attack, counting from 0 36 | 37 | ## Leakage model 38 | ## (these parameters correspond to function names in lracpa module) 39 | averagingFunction = roundXOR_valueForAveraging # for CPA and LRA 40 | intermediateFunction = roundXOR_targetVariable # for CPA and LRA 41 | leakageFunction = leakageModelHW # for CPA 42 | basisFunctionsModel = basisModelSingleBits # for LRA 43 | 44 | ## Known key for ranking 45 | knownKey = 0x8A7400A03230DA28 46 | encrypt = True 47 | 48 | # get the known key 49 | roundKeyNum = 1 50 | if (encrypt == False): 51 | roundKeyNum = 16 52 | roundKey = computeRoundKeys(knownKey, roundKeyNum)[roundKeyNum-1] 53 | knownKeyChunk = roundKeyChunk(roundKey, SboxNum) 54 | 55 | ################################################## 56 | ### 1. Log the parameters 57 | 58 | print "---\nAttack parameters" 59 | print "Averaging function :", averagingFunction.__name__ 60 | print "Intermediate function :", intermediateFunction.__name__ 61 | print "CPA leakage function :", leakageFunction.__name__ 62 | print "LRA basis functions :", basisFunctionsModel.__name__ 63 | print "Encryption :", encrypt 64 | print "S-box number :", SboxNum 65 | print "Known key : " + format(knownKey, "#018x") 66 | print "Known round key : " + format(roundKey, '#014x'), 67 | print '[', 68 | for i in range(8): 69 | print format(roundKeyChunk(roundKey, i), '#04x'), 70 | print ']' 71 | 72 | 73 | ################################################# 74 | ### 2. Load samples and data 75 | 76 | # Readout 77 | print "---\nLoading " + tracesetFilename 78 | t0 = time.clock() 79 | npzfile = np.load(tracesetFilename) 80 | data = npzfile['data'][0:N] 81 | traces = npzfile['traces'][0:N,sampleRange[0]:sampleRange[1]] 82 | t1 = time.clock() 83 | timeLoad = t1 - t0 84 | 85 | # convert data byte arrays to integers (more convenient for DES) 86 | print "Converting data..." 87 | datanew = [] 88 | for i in range(0, len(data)): 89 | datanew.append(struct.unpack('!Q', data[i][0:8].tostring())[0]) 90 | data = datanew # old data will be garbage-collected 91 | 92 | # Log traceset parameters 93 | (numTraces, traceLength) = traces.shape 94 | print "Number of traces loaded :", numTraces 95 | print "Trace length :", traceLength 96 | print "Loading time : %0.2f s" % timeLoad 97 | 98 | ################################################# 99 | ### 3. Attack with fixed amount of traces 100 | ''' 101 | print "---\nAttack" 102 | 103 | # perform conditional averaging 104 | CondAver = ConditionalAveragerDes(1024, traceLength) 105 | for i in range(N): 106 | CondAver.addTrace(data[i], traces[i], averagingFunction, SboxNum) 107 | (avdata, avtraces) = CondAver.getSnapshot() 108 | 109 | # CPA 110 | CorrTraces = cpaDES(avdata, avtraces, intermediateFunction, SboxNum, leakageFunction) 111 | 112 | # LRA 113 | R2, coefs = lraDES(avdata, avtraces, intermediateFunction, SboxNum, basisFunctionsModel) 114 | 115 | ### visualize results 116 | 117 | fig = plt.figure() 118 | 119 | # allocate grid 120 | axCPA = plt.subplot2grid((3, 1), (0, 0)) 121 | axLRA = plt.subplot2grid((3, 1), (1, 0)) 122 | axLRAcoefs = plt.subplot2grid((3, 1), (2, 0)) 123 | 124 | # CPA 125 | axCPA.plot(CorrTraces.T, color = 'grey') 126 | axCPA.plot(CorrTraces[knownKeyChunk, :], 'r') 127 | axCPA.set_xlim([0, traceLength]) 128 | 129 | # LRA 130 | axLRA.plot(R2.T, color = 'grey') 131 | axLRA.plot(R2[knownKeyChunk, :], 'r') 132 | axLRA.set_xlim([0, traceLength]) 133 | 134 | # LRA coefs 135 | coefsKnownKey = np.array(coefs[knownKeyChunk]) 136 | axLRAcoefs.pcolormesh(coefsKnownKey[:,:-1].T, cmap="jet") 137 | axLRAcoefs.set_xlim([0, traceLength]) 138 | 139 | # labels 140 | fig.suptitle("CPA and LRA on %d traces" % N) 141 | axCPA.set_ylabel('Correlation') 142 | axLRA.set_ylabel('R2') 143 | axLRAcoefs.set_ylabel('Basis function (bit)') 144 | axLRAcoefs.set_xlabel('Time sample') 145 | 146 | plt.show() 147 | ''' 148 | ################################################# 149 | ### 4. Attack with evolving amount of traces 150 | 151 | print "---\nAttack" 152 | 153 | t0 = time.clock() 154 | 155 | # initialize the incremental averager 156 | CondAver = ConditionalAveragerDes(1024, traceLength) 157 | 158 | # allocate arrays for storing key rank evolution 159 | numSteps = int(np.ceil(N / np.double(evolutionStep))) 160 | keyRankEvolutionCPA = np.zeros(numSteps) 161 | keyRankEvolutionLRA = np.zeros(numSteps) 162 | 163 | # the incremental loop 164 | tracesToSkip = 20 # warm-up to avoid numerical problems for small evolution step 165 | for i in range (0, tracesToSkip - 1): 166 | CondAver.addTrace(data[i], traces[i], averagingFunction, SboxNum) 167 | for i in range(tracesToSkip - 1, N): 168 | CondAver.addTrace(data[i], traces[i], averagingFunction, SboxNum) 169 | 170 | if (((i + 1) % evolutionStep == 0) or ((i + 1) == N)): 171 | 172 | (avdata, avtraces) = CondAver.getSnapshot() 173 | 174 | CorrTraces = cpaDES(avdata, avtraces, intermediateFunction, SboxNum, leakageFunction) 175 | R2, coefs = lraDES(avdata, avtraces, intermediateFunction, SboxNum, basisFunctionsModel) 176 | #R2 = normalizeR2Traces(R2) 177 | 178 | print "---\nResults after %d traces" % (i + 1) 179 | print "CPA" 180 | CorrPeaks = np.max(np.abs(CorrTraces), axis=1) # global maximization, absolute value! 181 | CpaWinningCandidate = np.argmax(CorrPeaks) 182 | CpaWinningCandidatePeak = np.max(CorrPeaks) 183 | CpaCorrectCandidateRank = np.count_nonzero(CorrPeaks >= CorrPeaks[knownKeyChunk]) 184 | CpaCorrectCandidatePeak = CorrPeaks[knownKeyChunk] 185 | print "Winning candidate: 0x%02x, peak magnitude %f" % (CpaWinningCandidate, CpaWinningCandidatePeak) 186 | print "Correct candidate: 0x%02x, peak magnitude %f, rank %d" % (knownKeyChunk, CpaCorrectCandidatePeak, CpaCorrectCandidateRank) 187 | 188 | print "LRA" 189 | R2Peaks = np.max(R2, axis=1) # global maximization 190 | LraWinningCandidate = np.argmax(R2Peaks) 191 | LraWinningCandidatePeak = np.max(R2Peaks) 192 | LraCorrectCandidateRank = np.count_nonzero(R2Peaks >= R2Peaks[knownKeyChunk]) 193 | LraCorrectCandidatePeak = R2Peaks[knownKeyChunk] 194 | print "Winning candidate: 0x%02x, peak magnitude %f" % (LraWinningCandidate, LraWinningCandidatePeak) 195 | print "Correct candidate: 0x%02x, peak magnitude %f, rank %d" % (knownKeyChunk, LraCorrectCandidatePeak, LraCorrectCandidateRank) 196 | 197 | stepCount = int(np.floor(i / np.double(evolutionStep))) 198 | keyRankEvolutionCPA[stepCount] = CpaCorrectCandidateRank 199 | keyRankEvolutionLRA[stepCount] = LraCorrectCandidateRank 200 | 201 | t1 = time.clock() 202 | timeAll = t1 - t0 203 | 204 | print "---\nCumulative timing" 205 | print "%0.2f s" % timeAll 206 | 207 | # save the rank evolution for later processing 208 | #np.savez("results/keyRankEvolutionSbox%02d" % SboxNum, kreCPA=keyRankEvolutionCPA, kreLRA=keyRankEvolutionLRA, step=evolutionStep) 209 | 210 | ################################################# 211 | ### 5. Visualize results 212 | 213 | print "---\nPlotting..." 214 | 215 | fig = plt.figure() 216 | 217 | # allocate grid 218 | axCPA = plt.subplot2grid((3, 2), (0, 0)) 219 | axLRA = plt.subplot2grid((3, 2), (1, 0)) 220 | axLRAcoefs = plt.subplot2grid((3, 2), (2, 0)) 221 | axRankEvolution = plt.subplot2grid((2, 2), (0, 1), rowspan = 3) 222 | 223 | # compute trace nubmers for x axis (TODO: move into block 3) 224 | traceNumbers = np.arange(evolutionStep, N + 1, evolutionStep) 225 | 226 | # CPA 227 | axCPA.plot(CorrTraces.T, color = 'grey') 228 | if CpaWinningCandidate != knownKeyChunk: 229 | axCPA.plot(CorrTraces[CpaWinningCandidate, :], 'blue') 230 | axCPA.plot(CorrTraces[knownKeyChunk, :], 'r') 231 | axRankEvolution.plot(traceNumbers, keyRankEvolutionCPA, color = 'green') 232 | axCPA.set_xlim([0, traceLength]) 233 | 234 | # LRA 235 | axLRA.plot(R2.T, color = 'grey') 236 | if LraWinningCandidate != knownKeyChunk: 237 | axLRA.plot(R2[LraWinningCandidate, :], 'blue') 238 | axLRA.plot(R2[knownKeyChunk, :], 'r') 239 | axRankEvolution.plot(traceNumbers, keyRankEvolutionLRA, color = 'magenta') 240 | axLRA.set_xlim([0, traceLength]) 241 | 242 | # LRA coefs 243 | coefsKnownKey = np.array(coefs[knownKeyChunk]) 244 | axLRAcoefs.pcolormesh(coefsKnownKey[:,:-1].T, cmap="jet") 245 | axLRAcoefs.set_xlim([0, traceLength]) 246 | 247 | # labels 248 | fig.suptitle("CPA and LRA on %d traces" % N) 249 | axCPA.set_ylabel('Correlation') 250 | axLRA.set_ylabel('R2') 251 | axLRAcoefs.set_ylabel('Basis function (bit)') 252 | axLRAcoefs.set_xlabel('Time sample') 253 | axRankEvolution.set_ylabel('Correct key candidate rank') 254 | axRankEvolution.set_xlabel('Number of traces') 255 | axRankEvolution.set_title('Correct key rank evolution (global maximisation)') 256 | 257 | # Limits and tick labels for key rand evolution plot 258 | axRankEvolution.set_xlim([traceNumbers[int(np.ceil(tracesToSkip / np.double(evolutionStep))) - 1], N]) 259 | axRankEvolution.set_ylim([0, 64]) 260 | axRankEvolution.grid(b=True, which='both', color='0.65',linestyle='-') 261 | #axRankEvolution.ticklabel_format(style='sci', axis='x', scilimits=(0,0), useOffset=True) 262 | 263 | # Legend for rank evolution plot 264 | axRankEvolution.legend(['CPA', 'LRA'], loc='upper right') 265 | 266 | plt.show() -------------------------------------------------------------------------------- /compareperformance.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | Compare perfromance and output of LRA and CPA with and without conditional averaging 7 | ''' 8 | 9 | import numpy as np 10 | import matplotlib.pyplot as plt 11 | import time 12 | 13 | from aes import AES # interweb's SlowAES toolbox 14 | from lracpa import * # my LRA-CPA toolbox 15 | from condaveraes import * # incremental conditional averaging 16 | 17 | ################################################## 18 | ### 0. Configurable parameters 19 | 20 | ## Traceset, number of traces, and S-box to attack 21 | tracesetFilename = "traces/swaes_atmega_power.npz" 22 | sampleRange = (950, 1150) # range of samples to attack, in the format (low, high) 23 | N = 2000 # number of traces to attack (less or equal to the amount of traces in the file) 24 | offset = 0 # trace number to start from 25 | SboxNum = 0 # S-box to attack, counting from 0 26 | 27 | ## Leakage model 28 | ## (these parameters correspond to function names in lracpa module) 29 | intermediateFunction = sBoxOut # for CPA and LRA 30 | leakageFunction = leakageModelHW # for CPA 31 | basisFunctionsModel = basisModelSingleBits # for LRA 32 | 33 | ## Known key for ranking 34 | knownKeyStr = "2B7E151628AED2A6ABF7158809CF4F3C".decode("hex") # the correct key 35 | encrypt = True # to avoid selective commenting in the following lines below 36 | 37 | if encrypt: # for encryption, the first round key is as is 38 | knownKey = np.array(map(ord, knownKeyStr), dtype="uint8") 39 | else: # for decryption, need to run key expansion 40 | expandedKnownKey = AES().expandKey(map(ord, knownKeyStr), 16, 16 * 11) # this returs a list 41 | knownKey = np.array(expandedKnownKey[176-16:177], dtype="uint8") 42 | 43 | 44 | ################################################## 45 | ### 1. Log the parameters 46 | 47 | print "---\nAttack parameters" 48 | print "Intermediate function :", intermediateFunction.__name__ 49 | print "CPA leakage function :", leakageFunction.__name__ 50 | print "LRA basis functions :", basisFunctionsModel.__name__ 51 | print "Encryption :", encrypt 52 | print "S-box number :", SboxNum 53 | print "Known roundkey : 0x%s" % str(bytearray(knownKey)).encode("hex") 54 | 55 | 56 | ################################################# 57 | ### 2. Load samples and data 58 | 59 | # Readout 60 | print "---\nLoading " + tracesetFilename 61 | t0 = time.clock() 62 | npzfile = np.load(tracesetFilename) 63 | data = npzfile['data'][offset:offset + N,SboxNum] # selecting only the required byte 64 | traces = npzfile['traces'][offset:offset + N,sampleRange[0]:sampleRange[1]] 65 | t1 = time.clock() 66 | timeLoad = t1 - t0 67 | 68 | # Log traceset parameters 69 | (numTraces, traceLength) = traces.shape 70 | print "Number of traces loaded :", numTraces 71 | print "Trace length :", traceLength 72 | print "Loading time : %0.2f s" % timeLoad 73 | 74 | ################################################# 75 | ### 2. LRA and CPA with a fixed number of traces 76 | 77 | print "---\nAttacks with %d traces" % numTraces 78 | print "Running CPA...", 79 | t0 = time.clock() 80 | CorrTraces = cpaAES(data, traces, intermediateFunction, leakageFunction) 81 | t1 = time.clock() 82 | print "done in %f s" % (t1 - t0) 83 | print "Running LRA...", 84 | t0 = time.clock() 85 | (R2, coefs) = lraAES(data, traces, intermediateFunction, basisFunctionsModel) 86 | t1 = time.clock() 87 | print "done in %f s" % (t1 - t0) 88 | print "Normalizing LRA results...", 89 | R2norm = normalizeR2Traces(R2) 90 | print "done" 91 | 92 | print "---\nAttacks with %d traces and conditional averaging" % numTraces 93 | print "Performing conditional trace averaging...", 94 | t0 = time.clock() 95 | (avdata, avtraces) = conditionalAveragingAESSbox(data[0:numTraces], traces[0:numTraces]) 96 | t1 = time.clock() 97 | print "done in %f s" % (t1 - t0) 98 | print "Running CPA on averaged traces...", 99 | t0 = time.clock() 100 | CorrTracesAv = cpaAES(avdata, avtraces, intermediateFunction, leakageFunction) 101 | t1 = time.clock() 102 | print "done in %f s" % (t1 - t0) 103 | print "Running LRA on averaged traces...", 104 | t0 = time.clock() 105 | (R2Av, coefsav) = lraAES(avdata, avtraces, intermediateFunction, basisFunctionsModel) 106 | t1 = time.clock() 107 | print "done in %f s" % (t1 - t0) 108 | print "Normalizing LRA results...", 109 | R2AvNorm = normalizeR2Traces(R2Av) 110 | print "done" 111 | 112 | ### 3. visualize the result, highlighting the correct trace 113 | 114 | print "---\nPlotting..." 115 | fig, ax = plt.subplots(3,2,sharex=True, squeeze=True) 116 | 117 | WrongKeyRange = range(0, knownKey[SboxNum]) + range(knownKey[SboxNum] + 1, 256) 118 | 119 | ax[0][0].plot(CorrTraces[WrongKeyRange, :].T, color = 'grey') 120 | ax[0][0].plot(CorrTraces[knownKey[SboxNum], :], 'r') 121 | 122 | ax[1][0].plot(R2[WrongKeyRange, :].T, color = 'grey') 123 | ax[1][0].plot(R2[knownKey[SboxNum], :], 'r') 124 | 125 | ax[2][0].plot(R2norm[WrongKeyRange, :].T, color = 'grey') 126 | ax[2][0].plot(R2norm[knownKey[SboxNum], :], 'r') 127 | 128 | ax[0][1].plot(CorrTracesAv[WrongKeyRange, :].T, color = 'grey') 129 | ax[0][1].plot(CorrTracesAv[knownKey[SboxNum], :], 'r') 130 | 131 | ax[1][1].plot(R2Av[WrongKeyRange, :].T, color = 'grey') 132 | ax[1][1].plot(R2Av[knownKey[SboxNum], :], 'r') 133 | 134 | ax[2][1].plot(R2AvNorm[WrongKeyRange, :].T, color = 'grey') 135 | ax[2][1].plot(R2AvNorm[knownKey[SboxNum], :], 'r') 136 | 137 | # same vertical scales for correlation and R2 138 | ax[0][0].set_ylim(ax[0][1].get_ylim()) 139 | ax[1][0].set_ylim(ax[1][1].get_ylim()) 140 | 141 | fig.suptitle("CPA and LRA on %d traces" % numTraces) 142 | ax[0][0].set_title('Without cond. averaging') 143 | ax[0][1].set_title('With cond. averaging') 144 | ax[0][0].set_ylabel('Correlation') 145 | ax[1][0].set_ylabel('R2') 146 | ax[2][0].set_ylabel('Normalized R2') 147 | ax[2][0].set_xlabel('Time sample') 148 | ax[2][1].set_xlabel('Time sample') 149 | 150 | plt.show() -------------------------------------------------------------------------------- /condaveraes.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | Conditional averaging for AES. Very rough so far. 7 | 8 | TODO: 9 | - iterator (therefore loop inside) 10 | - automatic readout from a file 11 | ''' 12 | 13 | import numpy as np 14 | 15 | class ConditionalAveragerAesSbox: 16 | 17 | def __init__(self, numValues, traceLength): 18 | '''Allocate the matrix of averaged traces''' 19 | self.avtraces = np.zeros((numValues, traceLength)) 20 | self.counters = np.zeros(numValues) 21 | print 'ConditionalAverager: initialized for %d values and trace length %d' % (numValues, traceLength) 22 | 23 | def addTrace(self, data, trace): 24 | '''Add a single trace with corresponding single chunk of data''' 25 | if (self.counters[data] == 0): 26 | self.avtraces[data] = trace 27 | else: 28 | self.avtraces[data] = self.avtraces[data] + (trace - self.avtraces[data]) / self.counters[data] 29 | self.counters[data] += 1 30 | 31 | def getSnapshot(self): 32 | ''' return a snapshot of the average matrix''' 33 | avdataSnap = np.flatnonzero(self.counters) # get an vector of only _observed_ values 34 | avtracesSnap = self.avtraces[avdataSnap] # remove lines corresponding to non-observed values 35 | return avdataSnap, avtracesSnap 36 | -------------------------------------------------------------------------------- /condaverdes.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | Conditional averaging for DES 7 | ''' 8 | 9 | import numpy as np 10 | 11 | class ConditionalAveragerDes: 12 | 13 | def __init__(self, numValues, traceLength): 14 | '''Allocate the matrix of averaged traces''' 15 | self.avtraces = np.zeros((numValues, traceLength)) 16 | self.counters = np.zeros(numValues) 17 | print 'ConditionalAverager: initialized for %d values and trace length %d' % (numValues, traceLength) 18 | 19 | def addTrace(self, data, trace, dataFunction, sBoxNumber): 20 | '''Add a single trace with corresponding single chunk of data computed based on the given function''' 21 | 22 | x = dataFunction(data, sBoxNumber) 23 | 24 | if (self.counters[x] == 0): 25 | self.avtraces[x] = trace 26 | else: 27 | self.avtraces[x] = self.avtraces[x] + (trace - self.avtraces[x]) / self.counters[x] 28 | self.counters[x] += 1 29 | 30 | def getSnapshot(self): 31 | ''' return a snapshot of the average matrix''' 32 | avdataSnap = np.flatnonzero(self.counters) # get an vector of only _observed_ values 33 | avtracesSnap = self.avtraces[avdataSnap] # remove lines corresponding to non-observed values 34 | return avdataSnap, avtracesSnap 35 | -------------------------------------------------------------------------------- /cpavisualdemo.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | CPA visual demo with live evolution, with AES-128 traces from ATmega16 7 | ''' 8 | 9 | import numpy as np 10 | import matplotlib as mpl 11 | mpl.use('TkAgg') # enforcing the backend, otherwise fails on Mac OS X with its default "macosx" backend 12 | import matplotlib.pyplot as plt 13 | import matplotlib.patches as pch 14 | 15 | from lracpa import correlationTraces # my LRA-CPA toolbox 16 | 17 | ############################################################################## 18 | # Parameters 19 | 20 | numByte = 0 # byte to attack, from 0 to 15 21 | 22 | # range for the nubmer of traces to attack (there are 2000 traces) 23 | startTraces = 5 # less than 5 leads to numerical problems in correlation computation 24 | stopTraces = 100 25 | stepTraces = 1 26 | 27 | # range of samples to attack (traces are 2800 samples long) 28 | sampleRange = range(950,1100) 29 | 30 | 31 | ############################################################################## 32 | # Prerequisites 33 | 34 | # the correct key (for later metrics, if any) 35 | correctKey = np.array([0x2B,0x7E,0x15,0x16,0x28,0xAE,0xD2,0xA6,0xAB,0xF7,0x15,0x88,0x09,0xCF,0x4F,0x3C]) 36 | 37 | # hamming weight leakage model (pure) 38 | byteHammingWeight = np.load('data/bytehammingweight.npy') # HW table 39 | def leakageModel_HW(x): 40 | return byteHammingWeight[x] 41 | 42 | 43 | ############################################################################## 44 | # Preload and precompute 45 | 46 | # load AES S-Box 47 | sbox = np.load('data/aessbox.npy') 48 | 49 | # load samples and data 50 | npzfile = np.load('traces/swaes_atmega_power.npz') 51 | data = npzfile['data'] 52 | traces = npzfile['traces'][:,sampleRange] 53 | 54 | # output traceset parameters 55 | numTraces = traces.shape[0] 56 | traceLength = traces.shape[1] 57 | print "Number of traces: ", numTraces 58 | print "Trace length: ", traceLength 59 | 60 | # compute intermediate variable hypotheses for all the key candidates 61 | k = np.arange(0,256, dtype='uint8') 62 | H = np.zeros((256, len(data)), dtype='uint8') 63 | for i in range(256): 64 | H[i,:] = sbox[data[:, numByte] ^ k[i]] 65 | 66 | # compute leakage hypotheses for every 67 | HL = np.array(map(leakageModel_HW, H)).T # leakage model here can be changed 68 | 69 | 70 | ############################################################################## 71 | # CPA attack (interleaved with incremental plotting, so a bit of a mess) 72 | 73 | ### Graphics stuff 74 | # allocate a line object for every correlation trace and evolution trace 75 | hc = [] 76 | he = [] 77 | fig, (ax1,ax2) = plt.subplots(1,2, sharey=True) 78 | for i in range(256): 79 | hc.append(ax2.plot([],[], linewidth=2, color='grey')[0]) 80 | he.append(ax1.plot([],[], linewidth=2, color='grey')[0]) 81 | # put the text label for showing the winning key candidate 82 | ht = plt.text(10, -0.95, "", fontsize=18, fontweight='bold') 83 | ax1.set_ylabel('Correlation', fontsize=18) 84 | ax1.set_xlabel('Number of traces', fontsize=18) 85 | ax2.set_xlabel('Time sample', fontsize=18) 86 | ax1.tick_params(axis='both', which='major', labelsize=18) 87 | ax2.tick_params(axis='both', which='major', labelsize=18) 88 | # show the figure and save the background 89 | ax2.axis([0, traceLength, -1, 1]) 90 | ax1.axis([0, stopTraces, -1, 1]) 91 | fig.show() 92 | fig.canvas.draw() 93 | for i in range(256): 94 | hc[i].set_xdata(range(1,traceLength+1)) 95 | 96 | hp = pch.ConnectionPatch(xyA=(0,0), xyB =(0,0), coordsA='data', axesA=ax2, axesB=ax1, color='black', linestyle='dashed') 97 | ax2.add_artist(hp) 98 | 99 | # main loop; attack and graphics are inevitably interleaved 100 | corrTraces = np.empty([256, traceLength]); 101 | guessedKeyBytePrev = 0 102 | for n in range(startTraces, stopTraces, stepTraces): 103 | 104 | # compute correlation traces, update them in the plot 105 | corrTraces = correlationTraces(traces[0:n], HL[0:n]) 106 | for i in range(0, 256): 107 | hc[i].set_ydata(corrTraces[i]) 108 | ax1.draw_artist(hc[i]) 109 | 110 | # determine the peaks and the most probable key candidate 111 | corrPeaks = np.max(corrTraces, axis=1) 112 | guessedKeyByte = np.argmax(corrPeaks) 113 | 114 | # update highlighting to the current winning key candidate 115 | hc[guessedKeyBytePrev].set_color('grey') 116 | hc[guessedKeyByte].set_color('red') 117 | he[guessedKeyBytePrev].set_color('grey') 118 | he[guessedKeyByte].set_color('red') 119 | guessedKeyBytePrev = guessedKeyByte 120 | 121 | for i in range(0, 256): 122 | he[i].set_xdata(np.append(he[i].get_xdata(), n)) 123 | he[i].set_ydata(np.append(he[i].get_ydata(), corrPeaks[i])) 124 | ax1.draw_artist(he[i]) 125 | 126 | # update the label 127 | label = "Key 0x%02x, peak %0.4f" % (guessedKeyByte, np.max(corrPeaks)) 128 | ht.set_text(label) 129 | ax2.draw_artist(ht) 130 | 131 | # show connecting line between the peak and the evolution plot 132 | hp.xy2 = (n, corrPeaks[guessedKeyByte]) 133 | hp.xy1 = (np.argmax(corrTraces[guessedKeyByte]), corrPeaks[guessedKeyByte]) 134 | ax2.draw_artist(hp) 135 | 136 | # refresh the plot 137 | # NOTE: no need to use blitting here or otherwise optimize 138 | # we even do not want to be very fast! 139 | fig.canvas.draw() 140 | fig.canvas.flush_events() # crucial: prevents freezing! 141 | 142 | plt.show() # to keep the figure open 143 | -------------------------------------------------------------------------------- /data/aesinvsbox.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ikizhvatov/pysca/36e136c93e87c2465abc7eeaf66aa030a43e4f1e/data/aesinvsbox.npy -------------------------------------------------------------------------------- /data/aessbox.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ikizhvatov/pysca/36e136c93e87c2465abc7eeaf66aa030a43e4f1e/data/aessbox.npy -------------------------------------------------------------------------------- /data/bytehammingweight.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ikizhvatov/pysca/36e136c93e87c2465abc7eeaf66aa030a43e4f1e/data/bytehammingweight.npy -------------------------------------------------------------------------------- /desutils.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | DES transformations required for DPA with conditional averaging. 7 | 8 | Self-tests included 9 | 10 | Uses minor chunks of code from pyDES-2.0.1 (https://twhiteman.netfirms.com/des.html) 11 | and DPA contest v1 DES example (https://svn.comelec.enst.fr/dpacontest/code/reference/). 12 | 13 | TODO: rewrite in Cython or in C using cyclic shifts and other natural bitwise 14 | operations; look at DES implementation in libtomcrypt as an example. 15 | ''' 16 | 17 | from operator import sub 18 | 19 | 20 | ############################################################################## 21 | # Core functionality 22 | 23 | ''' Bit permutations ''' 24 | def permuteBits(x, permutation, inputLength): 25 | ''' Permutes bits of x given a permutation table. Assumes that permutation table is 0-offset. 26 | The input bitlength is a parameter 27 | The output bitlength is determined by the permutation table 28 | ''' 29 | result = 0L 30 | for i in range(0, len(permutation)): 31 | result = ((result << 1) | 32 | ((x >> (inputLength - 1 - permutation[i])) & 1)) 33 | return result 34 | 35 | # These are bit permutaions to be used with permuteBits above, 36 | # not lookup tables. 37 | InitialPermutation = [ 38 | 57, 49, 41, 33, 25, 17, 9, 1, 39 | 59, 51, 43, 35, 27, 19, 11, 3, 40 | 61, 53, 45, 37, 29, 21, 13, 5, 41 | 63, 55, 47, 39, 31, 23, 15, 7, 42 | 56, 48, 40, 32, 24, 16, 8, 0, 43 | 58, 50, 42, 34, 26, 18, 10, 2, 44 | 60, 52, 44, 36, 28, 20, 12, 4, 45 | 62, 54, 46, 38, 30, 22, 14, 6 46 | ] 47 | RoundPermutation = [ 48 | 15, 6, 19, 20, 28, 11, 27, 16, 49 | 0, 14, 22, 25, 4, 17, 30, 9, 50 | 1, 7, 23, 13, 31, 26, 2, 8, 51 | 18, 12, 29, 5, 21, 10, 3, 24 52 | ] 53 | PC1Permutation = [ 54 | 56, 48, 40, 32, 24, 16, 8, 55 | 0, 57, 49, 41, 33, 25, 17, 56 | 9, 1, 58, 50, 42, 34, 26, 57 | 18, 10, 2, 59, 51, 43, 35, 58 | 62, 54, 46, 38, 30, 22, 14, 59 | 6, 61, 53, 45, 37, 29, 21, 60 | 13, 5, 60, 52, 44, 36, 28, 61 | 20, 12, 4, 27, 19, 11, 3 62 | ] 63 | PC2Permutation = [ 64 | 13, 16, 10, 23, 0, 4, 65 | 2, 27, 14, 5, 20, 9, 66 | 22, 18, 11, 3, 25, 7, 67 | 15, 6, 26, 19, 12, 1, 68 | 40, 51, 30, 36, 46, 54, 69 | 29, 39, 50, 44, 32, 47, 70 | 43, 48, 38, 55, 33, 52, 71 | 45, 41, 49, 35, 28, 31 72 | ] 73 | 74 | ''' S-box ''' 75 | def sBox(m, x): 76 | row = ((x & 0x20) >> 4) ^ (x & 1) 77 | col = (x & 0x1e) >> 1 78 | return SBoxLUT[m][16 * row + col] 79 | 80 | # This is a 6-to-4 bits lookup table. It is not directly 81 | # addressable with S-box input but requires a row-col transform, 82 | # see sBox() above. 83 | SBoxLUT = [ 84 | # S1 85 | [14, 4, 13, 1, 2, 15, 11, 8, 3, 10, 6, 12, 5, 9, 0, 7, 86 | 0, 15, 7, 4, 14, 2, 13, 1, 10, 6, 12, 11, 9, 5, 3, 8, 87 | 4, 1, 14, 8, 13, 6, 2, 11, 15, 12, 9, 7, 3, 10, 5, 0, 88 | 15, 12, 8, 2, 4, 9, 1, 7, 5, 11, 3, 14, 10, 0, 6, 13], 89 | 90 | # S2 91 | [15, 1, 8, 14, 6, 11, 3, 4, 9, 7, 2, 13, 12, 0, 5, 10, 92 | 3, 13, 4, 7, 15, 2, 8, 14, 12, 0, 1, 10, 6, 9, 11, 5, 93 | 0, 14, 7, 11, 10, 4, 13, 1, 5, 8, 12, 6, 9, 3, 2, 15, 94 | 13, 8, 10, 1, 3, 15, 4, 2, 11, 6, 7, 12, 0, 5, 14, 9], 95 | 96 | # S3 97 | [10, 0, 9, 14, 6, 3, 15, 5, 1, 13, 12, 7, 11, 4, 2, 8, 98 | 13, 7, 0, 9, 3, 4, 6, 10, 2, 8, 5, 14, 12, 11, 15, 1, 99 | 13, 6, 4, 9, 8, 15, 3, 0, 11, 1, 2, 12, 5, 10, 14, 7, 100 | 1, 10, 13, 0, 6, 9, 8, 7, 4, 15, 14, 3, 11, 5, 2, 12], 101 | 102 | # S4 103 | [7, 13, 14, 3, 0, 6, 9, 10, 1, 2, 8, 5, 11, 12, 4, 15, 104 | 13, 8, 11, 5, 6, 15, 0, 3, 4, 7, 2, 12, 1, 10, 14, 9, 105 | 10, 6, 9, 0, 12, 11, 7, 13, 15, 1, 3, 14, 5, 2, 8, 4, 106 | 3, 15, 0, 6, 10, 1, 13, 8, 9, 4, 5, 11, 12, 7, 2, 14], 107 | 108 | # S5 109 | [2, 12, 4, 1, 7, 10, 11, 6, 8, 5, 3, 15, 13, 0, 14, 9, 110 | 14, 11, 2, 12, 4, 7, 13, 1, 5, 0, 15, 10, 3, 9, 8, 6, 111 | 4, 2, 1, 11, 10, 13, 7, 8, 15, 9, 12, 5, 6, 3, 0, 14, 112 | 11, 8, 12, 7, 1, 14, 2, 13, 6, 15, 0, 9, 10, 4, 5, 3], 113 | 114 | # S6 115 | [12, 1, 10, 15, 9, 2, 6, 8, 0, 13, 3, 4, 14, 7, 5, 11, 116 | 10, 15, 4, 2, 7, 12, 9, 5, 6, 1, 13, 14, 0, 11, 3, 8, 117 | 9, 14, 15, 5, 2, 8, 12, 3, 7, 0, 4, 10, 1, 13, 11, 6, 118 | 4, 3, 2, 12, 9, 5, 15, 10, 11, 14, 1, 7, 6, 0, 8, 13], 119 | 120 | # S7 121 | [4, 11, 2, 14, 15, 0, 8, 13, 3, 12, 9, 7, 5, 10, 6, 1, 122 | 13, 0, 11, 7, 4, 9, 1, 10, 14, 3, 5, 12, 2, 15, 8, 6, 123 | 1, 4, 11, 13, 12, 3, 7, 14, 10, 15, 6, 8, 0, 5, 9, 2, 124 | 6, 11, 13, 8, 1, 4, 10, 7, 9, 5, 0, 15, 14, 2, 3, 12], 125 | 126 | # S8 127 | [13, 2, 8, 4, 6, 15, 11, 1, 10, 9, 3, 14, 5, 0, 12, 7, 128 | 1, 15, 13, 8, 10, 3, 7, 4, 12, 5, 6, 11, 0, 14, 9, 2, 129 | 7, 11, 4, 1, 9, 12, 14, 2, 0, 6, 10, 13, 15, 3, 5, 8, 130 | 2, 1, 14, 7, 4, 10, 8, 13, 15, 12, 9, 0, 3, 5, 6, 11] 131 | ] 132 | 133 | 134 | ''' Extension permutation, as a list of function per S-box. 135 | x is supposed to be a 32-bit wide integer. If not, function will work 136 | incorrectly. 137 | Calling example: ExtensionPermutationPerSbox[4](x) - retrive the part 138 | of E(x) corresponding to S-box 4''' 139 | ExpansionPerSbox = { 140 | 0 : lambda x : ((x >> 27) | (x << 5)) & 0x3f, 141 | 1 : lambda x : (x >> 23) & 0x3f, 142 | 2 : lambda x : (x >> 19) & 0x3f, 143 | 3 : lambda x : (x >> 15) & 0x3f, 144 | 4 : lambda x : (x >> 11) & 0x3f, 145 | 5 : lambda x : (x >> 7) & 0x3f, 146 | 6 : lambda x : (x >> 3) & 0x3f, 147 | 7 : lambda x : ((x << 1) | (x >> 31)) & 0x3f 148 | } 149 | 150 | '''Inverse P permutation as a list of bit-gathering functions per S-box. 151 | Generated using a helper function below.''' 152 | InversePermutationPerSbox = { 153 | 0 : lambda x : ((x >> 20) & 8) | ((x >> 13) & 4) | ((x >> 8) & 2) | ((x >> 1) & 1), 154 | 1 : lambda x : ((x >> 16) & 8) | ((x >> 2) & 4) | ((x >> 29) & 2) | ((x >> 14) & 1), 155 | 2 : lambda x : ((x >> 5) & 8) | ((x >> 14) & 4) | ((x >> 1) & 2) | ((x >> 26) & 1), 156 | 3 : lambda x : ((x >> 3) & 8) | ((x >> 10) & 4) | ((x >> 21) & 2) | ((x >> 31) & 1), 157 | 4 : lambda x : ((x >> 21) & 8) | ((x >> 16) & 4) | ((x >> 6) & 2) | ((x >> 29) & 1), 158 | 5 : lambda x : ((x >> 25) & 8) | ((x >> 1) & 4) | ((x >> 20) & 2) | ((x >> 13) & 1), 159 | 6 : lambda x : ((x << 3) & 8) | ((x >> 18) & 4) | ((x >> 9) & 2) | ((x >> 25) & 1), 160 | 7 : lambda x : ((x >> 24) & 8) | ((x >> 3) & 4) | ((x >> 16) & 2) | ((x >> 11) & 1) 161 | } 162 | 163 | ''' Key expansion ''' 164 | def computeRoundKeys(key, numberOfRounds): 165 | 166 | keyShifts = [1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1] 167 | mask28 = 0xfffffff 168 | 169 | # rotate left modulo 28 bits 170 | rol28 = lambda x, n: ((x << n) & mask28) | ((x & mask28) >> (28 - n)) 171 | 172 | permutedKey = permuteBits(key, PC1Permutation, 64) 173 | 174 | l = (permutedKey >> 28) & mask28 175 | r = permutedKey & mask28 176 | 177 | roundKeys = [] 178 | for i in range(numberOfRounds): 179 | l = rol28(l, keyShifts[i]) 180 | r = rol28(r, keyShifts[i]) 181 | lr = (l << 28) ^ r 182 | roundKey = permuteBits(lr, PC2Permutation, 56) 183 | roundKeys.append(roundKey) 184 | 185 | return roundKeys 186 | 187 | ''' Return n-th 6-bit chunk of the 48-bit round key ''' 188 | def roundKeyChunk(roundKey, n): 189 | return (roundKey >> (42 - 6 * n)) & 0x3f 190 | 191 | 192 | ############################################################################## 193 | # Tandem of functions for round in xor out intermediate. First functions 194 | # obtains value for conditional averaging. Second function obtains the value 195 | # of the target variable from that 196 | 197 | def roundXOR_valueForAveraging(input, sBoxNumber): 198 | ''' Compute the value for conditional averaging from input, for a given 199 | S-box number ''' 200 | 201 | # prepare the first round input halves 202 | permutedInput = permuteBits(input, InitialPermutation, 64) 203 | rightHalf = permutedInput & 0xFFFFFFFF 204 | leftHalf = permutedInput >> 32 205 | 206 | # 1. get 6-bit first part: the input chunks per S-box based on the structre value 207 | # 2. get 4-bit second part: the bits from XOR of left and right input 208 | # 3. concatenate to 10-bit value 209 | # TODO: consider a structure instead of concatenation 210 | a = ExpansionPerSbox[sBoxNumber](rightHalf) 211 | b = InversePermutationPerSbox[sBoxNumber](rightHalf ^ leftHalf) 212 | r = (a << 4) | b 213 | 214 | return r 215 | 216 | def roundXOR_targetVariable(averagingValue, keyChunk, sBoxNumber): 217 | ''' Compute the intermediate variable the value used for from key chunk, 218 | for a given S-box number ''' 219 | 220 | # unpack the value 221 | x = (averagingValue >> 4) & 0x3f 222 | y = averagingValue & 0xf 223 | 224 | # compute the intermediate value 225 | SBoxIn = x ^ keyChunk 226 | SBoxOut = sBox(sBoxNumber, SBoxIn) 227 | RoundInXorOutPerSBox = SBoxOut ^ y 228 | 229 | return RoundInXorOutPerSBox 230 | 231 | # Both merged into none, for attack without conditional averaging 232 | def roundXOR_allInOne(input, keyChunk, sBoxNumber): 233 | 234 | # prepare the first round input halves 235 | permutedInput = permuteBits(input, InitialPermutation, 64) 236 | rightHalf = permutedInput & 0xFFFFFFFF 237 | leftHalf = permutedInput >> 32 238 | 239 | # get S-box output 240 | a = ExpansionPerSbox[sBoxNumber](rightHalf) # returns 6 bits of S-box input 241 | SBoxIn = a ^ keyChunk 242 | SBoxOut = sBox(sBoxNumber, SBoxIn) 243 | 244 | # gather the input bits that need to be XORed with the S-box output 245 | b = InversePermutationPerSbox[sBoxNumber](rightHalf ^ leftHalf) 246 | 247 | # compute the XOR 248 | RoundInXorOutPerSBox = SBoxOut ^ b 249 | 250 | return RoundInXorOutPerSBox 251 | 252 | 253 | ############################################################################## 254 | # Self-creators 255 | 256 | def generateInversePermutationPerSbox(): 257 | ''' Helper used to generate the shifts. In the output, negative values should be manually replaced by a left shift! ''' 258 | print '\n--- generateInversePermutationPerSbox ---' 259 | 260 | initialPositionsPerSbox = [ 261 | [ 8, 16, 22, 30], 262 | [12, 27, 1, 17], 263 | [23, 15, 29, 5], 264 | [25, 19, 9, 0], 265 | [ 7, 13, 24, 2], 266 | [ 3, 28, 10, 18], 267 | [31, 11, 21, 6], 268 | [ 4, 26, 14, 20] 269 | ] 270 | finalPositions = [28, 29, 30, 31] 271 | 272 | for group in initialPositionsPerSbox: 273 | shifts = map(sub, finalPositions, group) # element-wise list subtraction 274 | print "((x >> %d) & 8) | ((x >> %d) & 4) | ((x >> %d) & 2) | ((x >> %d) & 1)" % (shifts[0], shifts[1], shifts[2], shifts[3]) 275 | 276 | 277 | ############################################################################## 278 | # Self-tests 279 | 280 | def testDesUtilities(): 281 | ''' Dump the state of the first round to compare against a reference implementation. 282 | Compare the inverse round permutation against the forward one. 283 | The output should look like: 284 | 285 | --- testDesUtilites --- 286 | L : 0x59e0bc92L 287 | R : 0xa69230c8L 288 | RK0: 0x8805bc20c812L 289 | Rt : 0x50d4a41a1651L 290 | Rtk: 0xd8d1183ade43L 291 | z : 0x789b6fef 292 | zp : 0x9c7eafebL 293 | Testing the inverse permutation 294 | z' : 0x789b6fefL 295 | Success! 296 | ''' 297 | print '\n--- testDesUtilites ---' 298 | 299 | # data from the first trace in TC8 PA training traceset 300 | key = 0x8a7400a03230da28L 301 | plaintext = 0x40a184466d9c52b7L 302 | ciphertext = 0x1cb5ca37b8a7a388L 303 | 304 | # key schedule 305 | roundKeys = computeRoundKeys(key, 16) 306 | k = roundKeys[0] 307 | 308 | # prepare the first round input halves (checked) 309 | permutedInput = permuteBits(plaintext, InitialPermutation, 64) 310 | rightHalf = permutedInput & 0xFFFFFFFF 311 | leftHalf = permutedInput >> 32 312 | print 'L : ' + hex(leftHalf) 313 | print 'R : ' + hex(rightHalf) 314 | print 'RK0: ' + hex(k) 315 | 316 | # expansion (checked) 317 | Rt = 0L 318 | for i in range(0, 8): 319 | a = ExpansionPerSbox[i](rightHalf) 320 | Rt = (Rt << 6) ^ a; 321 | print 'Rt : ' + hex(Rt) 322 | 323 | # key addition 324 | Rt = Rt ^ k 325 | print 'Rtk: ' + hex(Rt) 326 | 327 | # S-boxes 328 | z = 0L 329 | for i in range(0, 8): 330 | z ^= (sBox(7 - i, Rt & 0x3f) << (i * 4)) 331 | Rt = Rt >> 6 332 | print 'z : ' + hex(z) 333 | 334 | # permutation 335 | zp = permuteBits(z, RoundPermutation, 32) 336 | print 'zp : ' + hex(zp) 337 | 338 | # testing the inverse permutation 339 | print 'Testing the inverse permutation' 340 | zb = 0L 341 | for i in range(0, 8): 342 | zb ^= InversePermutationPerSbox[i](zp) << ((7 - i) * 4) 343 | print "z' : " + hex(zb) 344 | if (zb == z): 345 | print 'Success!' 346 | else: 347 | print 'Fail!' 348 | 349 | 350 | def dumpRoundKeys(): 351 | ''' Dump all round keys ''' 352 | print '\n--- dumpRoundKeys ---' 353 | 354 | key = 0x8a7400a03230da28L 355 | roundKeys = computeRoundKeys(key, 16) 356 | print 'Key : ' + format(key, '#018x') 357 | for i in range(16): 358 | print 'RK' + format(i, '02d') + ' : ' + format(roundKeys[i], '#014x'), 359 | print '[', 360 | for j in range(8): 361 | print format(roundKeyChunk(roundKeys[i], j), '#04x'), 362 | print ']' 363 | 364 | def dumpMiscValues(): 365 | ''' Print out the values, just in case ''' 366 | print '\n--- dumpMiscValues ---' 367 | 368 | Input = 0xA76DB873C63FE078 369 | KeyChunk = 0x2B 370 | print "Input:", hex(Input) 371 | print "Key chunk:", hex(KeyChunk) 372 | 373 | print "Values for averaging and target variables:" 374 | for i in range(0, 8): 375 | r = roundXOR_valueForAveraging(Input, i) 376 | t = roundXOR_targetVariable(r, KeyChunk, i) 377 | print "0x%04x, 0x%04x" % (r, t) 378 | 379 | print "Initial permutation" 380 | print hex(permuteBits(Input, InitialPermutation, 64)) 381 | 382 | RightHalf = Input & 0xFFFFFFFF 383 | 384 | print "Expansion per S-box:", 385 | print hex(ExpansionPerSbox[0](RightHalf)), 386 | print hex(ExpansionPerSbox[1](RightHalf)), 387 | print hex(ExpansionPerSbox[2](RightHalf)), 388 | print hex(ExpansionPerSbox[3](RightHalf)), 389 | print hex(ExpansionPerSbox[4](RightHalf)), 390 | print hex(ExpansionPerSbox[5](RightHalf)), 391 | print hex(ExpansionPerSbox[6](RightHalf)), 392 | print hex(ExpansionPerSbox[7](RightHalf)) 393 | 394 | print "Inverse permutation per S-box:", 395 | print hex(InversePermutationPerSbox[0](RightHalf)), 396 | print hex(InversePermutationPerSbox[1](RightHalf)), 397 | print hex(InversePermutationPerSbox[2](RightHalf)), 398 | print hex(InversePermutationPerSbox[3](RightHalf)), 399 | print hex(InversePermutationPerSbox[4](RightHalf)), 400 | print hex(InversePermutationPerSbox[5](RightHalf)), 401 | print hex(InversePermutationPerSbox[6](RightHalf)), 402 | print hex(InversePermutationPerSbox[7](RightHalf)) 403 | 404 | 405 | ############################################################################## 406 | # Entrypoint for self-testing 407 | 408 | if __name__ == "__main__": 409 | testDesUtilities() 410 | dumpRoundKeys() 411 | dumpMiscValues() 412 | generateInversePermutationPerSbox() 413 | -------------------------------------------------------------------------------- /howto/HOWTO.md: -------------------------------------------------------------------------------- 1 | # Pysca toolbox HOWTO 2 | 3 | This is a walkthrough showing how to put pysca in action with the provided example tracesets. It does not go deep under the hood; if you like that, the best way is to dive into the code starting from the scripts used here. 4 | 5 | ## Environment setup 6 | 7 | Pysca needs python 2.7 with numpy and matplotlib, and jupyter if you like to work with notebook format in a browser. Here is how to create a minimal environment for pysca with Anaconda python distribution in Linux and activate it: 8 | 9 | elbrus:pysca ilya$ conda create --name py27min python=2.7 numpy matplotlib jupyter 10 | [... all the conda printouts will be here ..] 11 | elbrus:pysca ilya$ source activate py27min 12 | (py27min) elbrus:pysca ilya$ 13 | 14 | Note that Anaconda provides numpy built against Intel MKL (Math Kernel Library), which is essential for performance. If you use other python dstribution, ensure that you use numpy-MKL. 15 | 16 | ## Traceset conversion 17 | 18 | Convert an example set of power traces obtained from a software AES running on an ATmega microcontroller. 19 | 20 | (py27min) elbrus:pysca ilya$ python trs2npz.py traces\swaes_atmega_power 21 | Number of traces: 2000 22 | Samples per trace: 2800 23 | Samples datatype: int8 24 | Data bytes: 16 25 | Trace block size: 26 | Header size: 27 | Preallocating arrays 28 | Populating arrays 29 | Saving file 30 | Done 31 | 32 | ## CPA and LRA attacks on SW AES 33 | 34 | For this example we will use the commad line script. Execute the script that performs the attacks, recovering a single key byte. 35 | 36 | (py27min) elbrus:pysca ilya$ attackaessbox.py 37 | --- 38 | Attack parameters 39 | Intermediate function : sBoxOut 40 | CPA leakage function : leakageModelHW 41 | LRA basis functions : basisModelSingleBits 42 | Encryption : True 43 | S-box number : 2 44 | Known key : 0x2b7e151628aed2a6abf7158809cf4f3c 45 | Known roundkey : 0x2b7e151628aed2a6abf7158809cf4f3c 46 | --- 47 | Loading traces/swaes_atmega_power.npz 48 | Number of traces loaded : 100 49 | Trace length : 700 50 | Loading time : 0.03 s 51 | --- 52 | Attack 53 | ConditionalAverager: initialized for 256 values and trace length 700 54 | --- 55 | Results after 20 traces 56 | CPA 57 | Winning candidate: 0xd3, peak magnitude 0.827157 58 | Correct candidate: 0x15, peak magnitude 0.748879, rank 22 59 | LRA 60 | Winning candidate: 0x7d, peak magnitude 0.951581 61 | Correct candidate: 0x15, peak magnitude 0.884216, rank 50 62 | --- 63 | [...] 64 | Results after 100 traces 65 | CPA 66 | Winning candidate: 0x15, peak magnitude 0.481228 67 | Correct candidate: 0x15, peak magnitude 0.481228, rank 1 68 | LRA 69 | Winning candidate: 0x15, peak magnitude 0.512743 70 | Correct candidate: 0x15, peak magnitude 0.512743, rank 1 71 | --- 72 | Cumulative timing 73 | 24.56 s 74 | --- 75 | Plotting... 76 | 77 | Observe the result visualization. The plots show results of CPA (correlation traces) and LRA (R2 traces and matrix of basis fucntion coefficients characterising the leakage function) for the maximum amonut of traces, and evolution of the correct key candidate rank with the increasing amount of traces. 78 | 79 | 80 | 81 | The parameters of the attack can be adjusted in the configuration section of the script. 82 | 83 | ## CPA and LRA attack on HW DES 84 | 85 | For this example, we will use another (convenient) way to work: a Jupyter noteboook in a browser. Launch the notebook server: 86 | 87 | (py27min) elbrus:pysca ilya$ jupyter notebook 88 | [I 13:15:38.823 NotebookApp] Serving notebooks from local directory: /Users/ilya/pysca 89 | [I 13:15:38.823 NotebookApp] 0 active kernels 90 | [I 13:15:38.824 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/?token=a75f5aa53be646d4a96bedc760728c9baea3a3a72b9111be 91 | [I 13:15:38.824 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). 92 | 93 | You will see the browser popping up with the directory contents, with two .ipynb notebooks. 94 | 95 | 96 | 97 | Open the notebook with the attack on DES round XOR. 98 | 99 | 100 | 101 | The code in the notebook is arranged in cells. Here is the cell with attack settings: 102 | 103 | 104 | 105 | The example traceset comes in npz format. Execute cells one-by-one and get an attack result plot in-line. 106 | 107 | 108 | 109 | This is the instantaneous attack result. To see the evolution, you can proceed with the following cells. 110 | 111 | This is it so far for the basics. 112 | -------------------------------------------------------------------------------- /howto/howto-notebook-des-result.png: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:7eff88206ab3739f9e481125c7ded913a6ed4a43b35bedaf660280221fa10cab 3 | size 193420 4 | -------------------------------------------------------------------------------- /howto/howto-notebook-des-settings.png: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:90a2a2f314d0196eb11a5079af5985fe3880f3ddd744f83433a17b4cab16cf6a 3 | size 247090 4 | -------------------------------------------------------------------------------- /howto/howto-notebook-des.png: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:677a9713e893fb7a93f3cce3a0b447af8bef040056affa7c69ccc18a2f6ec640 3 | size 208744 4 | -------------------------------------------------------------------------------- /howto/howto-notebooks.png: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:87667d694141dffc519ec10176924ad828b26ab8f17ff7209540a3f853c97886 3 | size 103662 4 | -------------------------------------------------------------------------------- /howto/howto-script-aes-result.png: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:88abce3231c1903583727eb9bf756d2abbd9373ae532277666eca26e9b5e420d 3 | size 144628 4 | -------------------------------------------------------------------------------- /lracpa.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | This file is a library of functions for CPA and LRA. The attacks are supposed to be 7 | implemented as scripts importing this library. 8 | 9 | AES S-box output and DES per-S-box round XOR are supported as target variables. 10 | 11 | Leakage functions for CPA and basis function models for LRA are defiend here as well. 12 | 13 | Non-profiled LRA is implemented a la ASIACRYPT'13 paper [https://eprint.iacr.org/2013/794]. 14 | Implementation uses manual OLS (dot-products and matrix inversion, relying on numpy-MKL 15 | efficient implementation). 16 | ''' 17 | 18 | import numpy as np 19 | 20 | # preload the precomputed lookup tables, to avoid bloating of this code 21 | sbox = np.load('data/aessbox.npy') # AES S-box 22 | invsbox = np.load('data/aesinvsbox.npy') # AES inverse S-box 23 | byteHammingWeight = np.load('data/bytehammingweight.npy') # HW of a byte 24 | 25 | 26 | ############################################################################# 27 | ### Common for both attacks 28 | 29 | # Functions for computing intermediate values 30 | # data is a 1-D array, keyByte is a scalar 31 | def sBoxOut(data, keyByte): 32 | sBoxIn = data ^ keyByte 33 | return sbox[sBoxIn] 34 | def sBoxInXorOut(data, keyByte): 35 | sBoxIn = data ^ keyByte 36 | return sBoxIn ^ sbox[sBoxIn] 37 | def invSboxOut(data, keyByte): 38 | sBoxIn = data ^ keyByte 39 | return invsbox[sBoxIn] 40 | def invSboxInXorOut(data, keyByte): 41 | sBoxIn = data ^ keyByte 42 | return sBoxIn ^ invsbox[sBoxIn] 43 | 44 | ############################################################################## 45 | ### A. LRA attack stuff 46 | 47 | ### Leakge modelling 48 | # These functions do 2 things in the same place: 49 | # 1. define basis functions gi(x) of a leakage model for a byte x: 50 | # b0 x g0(x) + b1 x g1(x) + ... + bn x gn(x) 51 | # 2. compute and return the values of gi(x), such that they can be used 52 | # later to obtain rows of the matrix for linear regression 53 | # Note that column of ones is included! 54 | # Note also that not all the functions are currently compatible with the code 55 | # in the CPA and LRA functions because latter use wrapper functions for 56 | # incremental binding. Only those are compatible that take a second bitWidth 57 | # argument. 58 | # TODO: make all functions compatible with incremental binding. 59 | 60 | 61 | # A simple linear model - sum of bits with different coefficients: 62 | # gi = xi, 0 <= i < bitWidth. 63 | def basisModelSingleBits(x, bitWidth): 64 | g = [] 65 | for i in range(0, bitWidth): 66 | bit = (x >> i) & 1 # this is the definition: gi = [bit i of x] 67 | g.append(bit) 68 | g.append(1) 69 | return g 70 | 71 | # Invididual bits and all pairwise products of bits 72 | def basisModelSingleBitsAndPairs(x, bitWidth): 73 | g = [] 74 | for i in range(0, bitWidth): 75 | # append single bits 76 | bit = (x >> i) & 1 # this is the definition: gi = [bit i of x] 77 | g.append(bit) 78 | # append pairs 79 | for j in range(i + 1, bitWidth): 80 | otherbit = (x >> j) & 1 81 | bitproduct = bit * otherbit 82 | g.append(bitproduct) 83 | g.append(1) 84 | return g 85 | 86 | # A Hamming weight model: g0 = HW(x) 87 | def basisModelHW(x): 88 | g = [] 89 | hw = byteHammingWeight[x] # this is the definition: gi = HW(x) 90 | g.append(hw) 91 | g.append(1) 92 | return g 93 | 94 | # An 'all 256 bit combinations' model: 95 | # a) helper from http://wiki.python.org/moin/BitManipulation 96 | def parityOf(int_type): 97 | parity = 0 98 | while (int_type): 99 | parity = ~parity 100 | int_type = int_type & (int_type - 1) 101 | if (parity != 0): # to convert -1 to 1 102 | parity = 1 103 | return(parity) 104 | # b) the model itself 105 | def basisModel256(x): 106 | g = [] 107 | # note that we start from 1 to exclude case 0 which means the function 108 | # does not depend on any bit of x, i.e. a constant - we will add the 109 | # constant explicitly later as the last column. 110 | for i in np.arange(1, 256, dtype='uint8'): 111 | xmasked = x & i 112 | gi = parityOf(xmasked) 113 | g.append(gi) 114 | g.append(1) 115 | return g 116 | 117 | # LRA attack on AES 118 | # data - 1-D array of input bytes 119 | # traces - 2-D array of traces 120 | # intermediateFunction - one of the functions like sBoxOut above in the common section 121 | # basisFunctionsModel - one of the functions like basisModelSingleBits above 122 | # in this section 123 | # TODO parametrize hard-coded values such as 256, 8 124 | def lraAES(data, traces, intermediateFunction, basisFunctionsModel): 125 | 126 | ### 0. some helper variables 127 | (numTraces, traceLength) = traces.shape 128 | 129 | # define a wrapper for currying (incremental parameter binding) 130 | def basisFunctionsModelWrapper(y): 131 | def basisFunctionsModelCurry(x): 132 | return basisFunctionsModel(x, y) 133 | return basisFunctionsModelCurry 134 | 135 | ### 1: compute SST over the traces 136 | SStot = np.sum((traces - np.mean(traces, 0)) ** 2, 0) 137 | 138 | ### 2. The main attack loop 139 | 140 | # preallocate arrays 141 | SSreg = np.empty((256, traceLength)) # Sum of Squares due to regression 142 | E = np.empty(numTraces) # expected values 143 | 144 | allCoefs = [] # placeholder for regression coefficients 145 | 146 | # per-keycandidate loop 147 | for k in np.arange(0, 256, dtype='uint8'): 148 | 149 | # predict intermediate variable 150 | intermediateVariable = intermediateFunction(data, k) 151 | 152 | # buld equation system 153 | M = np.array(map(basisFunctionsModelWrapper(8), intermediateVariable)) 154 | 155 | # some precomputations before the per-sample loop 156 | P = np.dot(np.linalg.inv(np.dot(M.T, M)), M.T) 157 | #Q = np.dot(M, P) 158 | 159 | coefs = [] # placeholder for regression coefficients 160 | 161 | # per-sample loop: solve the system for each time moment 162 | for u in range(0,traceLength): 163 | 164 | # if do not need coefficients beta - use precomputed value 165 | #np.dot(Q, traces[:,u], out=E) 166 | 167 | # if need the coefficients - do the multiplication using 168 | # two dot products and let the functuion return beta alongside R2 169 | beta = np.dot(P, traces[:,u]) 170 | coefs.append(beta) 171 | E = np.dot(M, beta) 172 | 173 | SSreg[k,u] = np.sum((E - traces[:,u]) ** 2) 174 | 175 | allCoefs.append(coefs) 176 | #print 'Done with candidate', k 177 | 178 | ### 3. compute Rsquared 179 | R2 = 1 - SSreg / SStot[None, :] 180 | 181 | return R2, allCoefs 182 | 183 | # LRA attack on DES 184 | # data - array of inputs (format depends on intermediateFunction) 185 | # traces - 2-D array of traces 186 | # intermediateFunction - one of functions like sBoxOut above in the common section 187 | # sBoxNumber - DES S-box to attack 188 | # basisFunctionsModel - one of function like basisModel9 above in this section 189 | # TODO parametrize hard-coded values such as 64, 6, refactor to merge common part with AES 190 | def lraDES(data, traces, intermediateFunction, sBoxNumber, basisFunctionsModel): 191 | 192 | ### 0. some helper variables 193 | (numTraces, traceLength) = traces.shape 194 | 195 | # define a wrapper (currying) for incremental parameter binding 196 | def basisFunctionsModelWrapper(y): 197 | def basisFunctionsModelCurry(x): 198 | return basisFunctionsModel(x, y) 199 | return basisFunctionsModelCurry 200 | 201 | ### 1: compute SST over the traces 202 | SStot = np.sum((traces - np.mean(traces, 0)) ** 2, 0) 203 | 204 | ### 2. The main attack loop 205 | 206 | # preallocate arrays 207 | SSreg = np.empty((64, traceLength)) # Sum of Squares due to regression 208 | E = np.empty(numTraces) # expected values 209 | 210 | allCoefs = [] # placeholder for regression coefficient 211 | 212 | # per-keycandidate loop 213 | for k in np.arange(0, 64, dtype='uint8'): 214 | 215 | # predict intermediate variable for the current key candiate value 216 | intermediateVariable = np.zeros(len(data), dtype='uint8') 217 | for j in range(0, len(data)): 218 | intermediateVariable[j] = intermediateFunction(data[j], k, sBoxNumber) 219 | 220 | # buld equation system 221 | M = np.array(map(basisFunctionsModelWrapper(4), intermediateVariable)) 222 | 223 | # some precomputations before the per-sample loop 224 | P = np.dot(np.linalg.inv(np.dot(M.T, M)), M.T) 225 | #Q = np.dot(M, P) 226 | 227 | coefs = [] # placeholder for regression coefficients 228 | 229 | # per-sample loop: solve the system for each time moment 230 | for u in range(0,traceLength): 231 | 232 | # if do not need coefficients beta - use precomputed value 233 | #np.dot(Q, traces[:,u], out=E) 234 | 235 | # if need the coefficients - do the multiplication using 236 | # two dot products and let the functuion return beta alongside R2 237 | beta = np.dot(P, traces[:,u]) 238 | coefs.append(beta) 239 | E = np.dot(M, beta) 240 | 241 | SSreg[k,u] = np.sum((E - traces[:,u]) ** 2) 242 | 243 | allCoefs.append(coefs) 244 | #print 'Done with candidate', k 245 | 246 | ### 3. compute Rsquared 247 | R2 = 1 - SSreg / SStot[None, :] 248 | 249 | return R2, allCoefs 250 | 251 | # convert R2 to adjusted R2 (https://en.wikipedia.org/wiki/Coefficient_of_determination#Adjusted_R2) 252 | # n - number of samples 253 | # p - the total number of regressors in the linear model (i.e. basis functions), excluding the linear term 254 | def adjustedR2(R2, n, p): 255 | R2adj = 1 - ((1 - R2 ** 2) * (n - 1) / np.double(n - p - 1)) 256 | return R2adj 257 | 258 | # normalize the matrix of distinguisher traces according to ASIACRYPT'13 proposal 259 | def normalizeR2Traces(R2): 260 | R2norm = np.empty(R2.shape) 261 | traceLength = R2.shape[1] 262 | for i in range(0,traceLength): # TODO should be possible to do it in one line without a loop 263 | R2norm[:,i] = (R2[:,i] - np.mean(R2[:,i])) / np.var(R2[:,i]) 264 | return R2norm 265 | 266 | ############################################################################## 267 | ### A. CPA attack stuff 268 | 269 | def leakageModelHW(x): 270 | return byteHammingWeight[x] 271 | 272 | # correlation trace computation as improved by StackOverflow community 273 | # O - matrix of observed leakage (i.e. traces) 274 | # P - column of predictions 275 | # returns a correlation trace 276 | def correlationTraceSO(O, P): 277 | n = P.size 278 | DO = O - (np.einsum('ij->j', O, dtype='float64', optimize='optimal') / np.double(n)) 279 | DP = P - (np.einsum('i->', P, dtype='float64', optimize='optimal') / np.double(n)) 280 | tmp = np.einsum('ij,ij->j', DO, DO, optimize='optimal') 281 | tmp *= np.einsum('i,i->', DP, DP, optimize='optimal') 282 | return np.dot(DP, DO) / np.sqrt(tmp) 283 | 284 | # Even faster correlation trace computation 285 | # Takes the full matrix of predictions instead of just a column 286 | # O - (n,t) array of n traces with t samples each 287 | # P - (n,m) array of n predictions for each of the m candidates 288 | # returns an (m,t) correaltion matrix of m traces t samples each 289 | def correlationTraces(O, P): 290 | (n, t) = O.shape # n traces of t samples 291 | (n_bis, m) = P.shape # n predictions for each of m candidates 292 | 293 | DO = O - (np.einsum("nt->t", O, dtype='float64', optimize='optimal') / np.double(n)) # compute O - mean(O) 294 | DP = P - (np.einsum("nm->m", P, dtype='float64', optimize='optimal') / np.double(n)) # compute P - mean(P) 295 | 296 | numerator = np.einsum("nm,nt->mt", DP, DO, optimize='optimal') 297 | tmp1 = np.einsum("nm,nm->m", DP, DP, optimize='optimal') 298 | tmp2 = np.einsum("nt,nt->t", DO, DO, optimize='optimal') 299 | tmp = np.einsum("m,t->mt", tmp1, tmp2, optimize='optimal') 300 | denominator = np.sqrt(tmp) 301 | 302 | return numerator / denominator 303 | 304 | # CPA attack 305 | # data - 1-D array of input bytes 306 | # traces - 2-D array of traces 307 | # intermediateFunction - one of functions like sBoxOut above in the common section 308 | # leakageFunction - one of the fucntions like leakgeModelHW above in this section 309 | def cpaAES(data, traces, intermediateFunction, leakageFunction): 310 | 311 | traceLength = traces.shape[1] 312 | 313 | # compute intermediate variable predictions 314 | k = np.arange(0,256, dtype='uint8') # key chunk candidates 315 | H = np.zeros((256, len(data)), dtype='uint8') # intermediate variable predictions 316 | for i in range(256): 317 | H[i,:] = intermediateFunction(data, k[i]) 318 | 319 | # compute leakage hypotheses for every all the key candidates 320 | HL = np.array(map(leakageFunction, H)).T # leakage model here (HW for now) 321 | 322 | CorrTraces = correlationTraces(traces, HL) 323 | 324 | return CorrTraces 325 | 326 | # CPA attack on DES 327 | # data - array of inputs (format depends on intermediateFunction) 328 | # traces - 2-D array of traces 329 | # intermediateFunction - one of functions like sBoxOut above in the common section 330 | # sBoxNumber - DES S-box to attack 331 | # leakageFunction - one of the fucntions like leakageModelHW above in this section 332 | def cpaDES(data, traces, intermediateFunction, sBoxNumber, leakageFunction): 333 | 334 | traceLength = traces.shape[1] 335 | 336 | # compute intermediate variable predictions 337 | k = np.arange(0,64, dtype='uint8') # key chunk candidates 338 | H = np.zeros((64, len(data)), dtype='uint8') # intermediate variable predictions 339 | for i in range(64): 340 | for j in range(0, len(data)): 341 | H[i,j] = intermediateFunction(data[j], k[i], sBoxNumber) 342 | 343 | # compute leakage hypotheses for every all the key candidates 344 | HL = np.array(map(leakageFunction, H)).T # leakage model here (HW for now) 345 | 346 | CorrTraces = correlationTraces(traces, HL) 347 | 348 | return CorrTraces 349 | -------------------------------------------------------------------------------- /results/attackaessbox_test.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ikizhvatov/pysca/36e136c93e87c2465abc7eeaf66aa030a43e4f1e/results/attackaessbox_test.png -------------------------------------------------------------------------------- /results/attackaessbox_test.txt: -------------------------------------------------------------------------------- 1 | In [104]: %run attackaessbox.py 2 | --- 3 | Attack parameters 4 | Intermediate function : sBoxOut 5 | CPA leakage function : leakageModelHW 6 | LRA basis functions : basisModel9 7 | Encryption : True 8 | S-box number : 1 9 | Known roundkey : 0x2b7e151628aed2a6abf7158809cf4f3c 10 | --- 11 | Loading traces/swaes_atmega_powertraces.npz 12 | Number of traces loaded : 20 13 | Trace length : 2800 14 | Loading time : 0.01 s 15 | --- 16 | Attack 17 | Performing conditional trace averaging... done in 0.00 s 18 | Running CPA on averaged traces... done in 0.07 s 19 | Running LRA on averaged traces... done in 9.47 s 20 | --- 21 | Results CPA 22 | Winning candidate: 0x20, peak magnitude 0.809684 23 | Correct candidate: 0x7e, peak magnitude 0.750731, rank 22 24 | --- 25 | Results LRA 26 | Winning candidate: 0xfa, peak magnitude 0.964337 27 | Correct candidate: 0x7e, peak magnitude 0.892681, rank 123 28 | --- 29 | Plotting... -------------------------------------------------------------------------------- /results/performance_logs.txt: -------------------------------------------------------------------------------- 1 | The following results were obtained on an Intel Core i5-2540M, 8GB RAM, 2 | Windows 7 x64, Python 2.7.8 x64, numpy-MKL 1.9.0. 3 | 4 | ---------------------- 5 | swaes_atmega_powertraces.trs 6 | 7 | In [3]: %run lra 8 | Traceset parameters 9 | Number of traces: 2000 10 | Trace length: 200 11 | --- 12 | Attacks with 1000 traces: 13 | Running CPA... done in 0.349224 s 14 | Running LRA... done in 37.254000 s 15 | Normalizing LRA results... done 16 | --- 17 | Attacks with 1000 traces and conditional averaging: 18 | Performing conditional trace averaging... done in 0.003820 s 19 | Running CPA on averaged traces... done in 0.057802 s 20 | Running LRA on averaged traces... done in 3.997455 s 21 | Normalizing LRA results... done 22 | --- 23 | Plotting... 24 | 25 | Speedup factor CPA: 5.67 26 | Speedup factor LRA: 9.3 27 | 28 | ---------------------- 29 | swaes_atmega_powertraces2_compressed.trs 30 | 31 | In [35]: %run lracpa_swaes.py 32 | Traceset parameters 33 | Number of traces: 10000 34 | Trace length: 274 35 | --- 36 | Attacks with 10000 traces 37 | Running CPA... done in 6.400619 s 38 | Running LRA... done in 3439.946043 s 39 | Normalizing LRA results... done 40 | --- 41 | Attacks with 10000 traces and conditional averaging 42 | Performing conditional trace averaging... done in 0.120957 s 43 | Running CPA on averaged traces... done in 0.086983 s 44 | Running LRA on averaged traces... done in 4.831128 s 45 | Normalizing LRA results... done 46 | --- 47 | Plotting... 48 | 49 | Speedup factor CPA: 30.78 50 | Speedup factor LRA: 694.64 51 | 52 | 53 | ---------------------- 54 | This one on the new lab machine (file was opened not the frist time so cached) 55 | ---------------------- 56 | In [14]: %run lracpa_swaes.py 57 | Loadingtraces/hwaes_xxx_winres_trimtrim940-100.npz 58 | Traceset parameters 59 | Number of traces: 7798433 60 | Trace length: 100 61 | Loading time: 6.67022741521 62 | --- 63 | Attacks with 7798433 traces and conditional averaging 64 | Performing conditional trace averaging... done in 76.101671 s 65 | Running CPA on averaged traces... done in 0.083418 s 66 | Running LRA on averaged traces... done in 3.642333 s 67 | Normalizing LRA results... done 68 | --- 69 | Plotting... 70 | 71 | ----------------------------- 72 | And the same for the laptop (file cached as well) 73 | See the effect of a faster single-core operation 74 | ----------------------------- 75 | In [2]: %run lracpa_swaes.py 76 | Loading traces/hwaes_xxx_winres_trimtrim940-100.npz 77 | Traceset parameters 78 | Number of traces: 7798433 79 | Trace length: 100 80 | Loading time: 8.26878899541 81 | --- 82 | Attacks with 7798433 traces and conditional averaging 83 | Performing conditional trace averaging... done in 61.314245 s 84 | Running CPA on averaged traces... done in 0.032915 s 85 | Running LRA on averaged traces... done in 2.942761 s 86 | Normalizing LRA results... done 87 | --- 88 | Plotting... -------------------------------------------------------------------------------- /results/performance_logs_bis.txt: -------------------------------------------------------------------------------- 1 | compareperformance.py script adjusted to the recent codebase 2 | 3 | The following results were obtained on an Intel Core i5-2540M, 8GB RAM, 4 | Windows 7 x64, Python 2.7.10 x64, numpy-MKL 1.10.1. 5 | 6 | ---------------------- 7 | In [6]: %run compareperformance.py 8 | --- 9 | Attack parameters 10 | Intermediate function : sBoxOut 11 | CPA leakage function : leakageModelHW 12 | LRA basis functions : basisModelSingleBits 13 | Encryption : True 14 | S-box number : 3 15 | Known roundkey : 0x2b7e151628aed2a6abf7158809cf4f3c 16 | --- 17 | Loading traces/swaes_atmega_powertraces.npz 18 | Number of traces loaded : 2000 19 | Trace length : 200 20 | Loading time : 0.03 s 21 | --- 22 | Attacks with 2000 traces 23 | Running CPA... done in 0.778006 s 24 | Running LRA... done in 20.568550 s 25 | Normalizing LRA results... done 26 | --- 27 | Attacks with 2000 traces and conditional averaging 28 | Performing conditional trace averaging... done in 0.018740 s 29 | Running CPA on averaged traces... done in 0.057710 s 30 | Running LRA on averaged traces... done in 3.075778 s 31 | Normalizing LRA results... done 32 | --- 33 | Plotting... 34 | 35 | ---------------------- 36 | In [7]: %run compareperformance.py 37 | --- 38 | Attack parameters 39 | Intermediate function : sBoxOut 40 | CPA leakage function : leakageModelHW 41 | LRA basis functions : basisModelSingleBits 42 | Encryption : True 43 | S-box number : 3 44 | Known roundkey : 0x2b7e151628aed2a6abf7158809cf4f3c 45 | --- 46 | Loading traces/swaes_atmega_powertraces2_compressed.npz 47 | Number of traces loaded : 10000 48 | Trace length : 274 49 | Loading time : 0.01 s 50 | --- 51 | Attacks with 10000 traces 52 | Running CPA... done in 5.389510 s 53 | Running LRA... done in 107.093492 s 54 | Normalizing LRA results... done 55 | --- 56 | Attacks with 10000 traces and conditional averaging 57 | Performing conditional trace averaging... done in 0.128395 s 58 | Running CPA on averaged traces... done in 0.075705 s 59 | Running LRA on averaged traces... done in 4.017093 s 60 | Normalizing LRA results... done 61 | --- 62 | Plotting... -------------------------------------------------------------------------------- /traces/hwdes_card8_power.npz: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:f737084c1b6bf1af7b025b602bed6be82163081e4e8b7e277e8b9518d169faf3 3 | size 2160370 4 | -------------------------------------------------------------------------------- /traces/swaes_atmega_power.trs: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:3257e8005007e1fa3506a6d5fc57d782feb69edfd0f3fc80c7e91d71280a3b0f 3 | size 5888128 4 | -------------------------------------------------------------------------------- /trs2npz.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This file is part of pysca toolbox, license is GPLv3, see https://www.gnu.org/licenses/gpl-3.0.en.html 3 | Author: Ilya Kizhvatov 4 | Version: 1.0, 2017-05-14 5 | 6 | Convert Inspector traceset into numpy array and save to npz. 7 | Reads the entire traceset into memory, so cannot deal with huge tracesets. 8 | 9 | External packages required: 10 | - numpy 11 | - Trace.py 12 | 13 | Versions of this file: 14 | v0.2 2014-10-31 Ilya: data conversion to unit64 made optional; some refactoring 15 | v0.1 2013-11-12 Ilya: initial 16 | ''' 17 | 18 | import argparse 19 | import numpy as np 20 | import struct 21 | import Trace as trs 22 | 23 | # Determine coding of samples in .trs traceset and return it in numpy format 24 | def determineTrsSampleCoding(ts): 25 | if ts._sampleCoding == ts.CodingByte: 26 | samplesDataType = "int8" 27 | elif ts._sampleCoding == ts.CodingShort: 28 | samplesDataType = "int16" 29 | elif ts._sampleCoding == ts.CodingInt: 30 | samplesDataType = "int32" 31 | elif ts._sampleCoding == ts.CodingFloat: 32 | samplesDataType = "float32" 33 | else: 34 | samplesDataType = None 35 | return samplesDataType 36 | 37 | # Print main metadata of the .trs traceset 38 | def printTrsMetadata(ts, samplesDataType): 39 | print("Number of traces:\t%d" % ts._numberOfTraces) 40 | print("Samples per trace:\t%d" % ts._numberOfSamplesPerTrace) 41 | print("Samples datatype:\t%s" % samplesDataType) 42 | print("Data bytes:\t\t%d" % ts._dataSpace) 43 | print("Trace block size:\t%d bytes" % ts._traceBlockSpace) 44 | print("Header size:\t\t%d bytes" % ts._traceBlockOffset) 45 | 46 | if __name__ == "__main__": 47 | 48 | parser = argparse.ArgumentParser(description='Convert Inspector 4 traceset into numpy array') 49 | parser.add_argument('-c', '--convertdata', action='store_true', help='convert data from byte array to uint64 chunks') 50 | parser.add_argument('filename', help='traceset file name without trs extension') 51 | args = parser.parse_args() 52 | 53 | ts = trs.TraceSet() 54 | ts.open(args.filename + ".trs") 55 | samplesDataType = determineTrsSampleCoding(ts) 56 | printTrsMetadata(ts, samplesDataType) 57 | 58 | # read out the traces 59 | print("Preallocating arrays") 60 | traces = np.empty(shape=(ts._numberOfTraces, ts._numberOfSamplesPerTrace), dtype = samplesDataType) 61 | data = np.empty(shape=(ts._numberOfTraces, ts._dataSpace), dtype = "uint8") 62 | print("Populating arrays") 63 | for i in range(ts._numberOfTraces): 64 | t = ts.getTrace(i) 65 | traces[i, :] = np.array(t._samples, dtype = samplesDataType) 66 | data[i, :] = np.array(t._data, dtype = "uint8") 67 | 68 | if args.convertdata: 69 | print("Gathering bytes to uint64's") 70 | wordBytes = 8 71 | structCommand = '!Q' 72 | numberOfWordsNecessary = (len(data[0]) + wordBytes - 1)//(wordBytes) 73 | datanew = np.empty((len(data),numberOfWordsNecessary), dtype='uint64') 74 | for i in range(0, len(data)): 75 | if(len(data[i]) < numberOfWordsNecessary*wordBytes): 76 | tempData = np.concatenate((np.zeros(numberOfWordsNecessary*wordBytes - len(data[i]), dtype="uint8"), data[i])) 77 | else: 78 | tempData = data[i] 79 | for j in range(numberOfWordsNecessary): 80 | datanew[i][j] = struct.unpack(structCommand, tempData[(j*wordBytes):((j+1)*wordBytes)].tostring())[0] 81 | data = datanew # old data will be garbage-collected 82 | 83 | print("Saving file") 84 | np.savez(args.filename, traces=traces, data=data) 85 | print("Done") --------------------------------------------------------------------------------