2023-12-19 18:19:01 +00:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Break AES using DPA with correlations\n",
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"\n",
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"You need:\n",
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"* `plaintext.txt`: all PT blocks, (one block per line, in hex, bytes separated by spaces)\n",
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"* `ciphertext.txt`: all CT blocks, (one block per line, in hex, bytes separated by spaces)\n",
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"* `traceLength.txt`: how many samples per trace (one decimal number)\n",
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"* `traces.bin`: raw measured traces, one byte per sample (uint8), all traces together continuously\n"
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]
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},
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{
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"cell_type": "code",
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2024-01-12 11:29:39 +00:00
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"execution_count": 1,
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2023-12-19 18:19:01 +00:00
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"metadata": {
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"id": "GEwwR12Gupsi"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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2024-01-12 11:29:39 +00:00
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"execution_count": 2,
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2023-12-19 18:19:01 +00:00
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"metadata": {
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"id": "8fW8nPQ5uyEO"
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},
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"outputs": [],
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"source": [
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"# AES SBOX\n",
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"sbox = np.array([\n",
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" 0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76,\n",
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" 0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0,\n",
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" 0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15,\n",
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" 0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75,\n",
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" 0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84,\n",
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" 0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF,\n",
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" 0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8,\n",
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" 0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2,\n",
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" 0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73,\n",
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" 0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB,\n",
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" 0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79,\n",
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" 0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08,\n",
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" 0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A,\n",
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" 0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E,\n",
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" 0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF,\n",
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" 0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16\n",
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" ], dtype='uint8')\n",
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"\n",
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"# Hamming weight lookup table\n",
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"hw_table = []\n",
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"for i in range(256):\n",
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" s = '{0:08b}'.format(i)\n",
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" hw_table.append(s.count('1'))\n",
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"hw_table = np.array(hw_table, 'uint8')\n",
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"\n",
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"# Correlation of two matrices\n",
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"def correlate(x, y):\n",
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" \"\"\"\n",
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" Correlate all columns from matrix x of shape (a,b)\n",
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" with all columns from matrix y of shape (a,c),\n",
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" creating correlation matrix C of shape (b,c).\n",
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" \n",
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" Originally matlab script by Jiri Bucek in NI-HWB.\n",
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" \"\"\"\n",
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" x = x - np.average(x, 0) # remove vertical averages\n",
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" y = y - np.average(y, 0) # remove vertical averages\n",
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" C = x.T @ y # (n-1) Cov(x,y)\n",
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" C = C / (np.sum(x**2, 0)**(1/2))[:,np.newaxis] # divide by (n-1) Var(x)\n",
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" C = C / (np.sum(y**2, 0)**(1/2)) # divide by (n-1) Var(y)\n",
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" return C\n",
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"\n",
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"# Load PT of CT from file\n",
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"def load_text(file_name):\n",
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" \"\"\"\n",
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" Load any text PT/CT from file containing hex strings with bytes \n",
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" separated by spaces, one block per line\n",
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" Output is a matrix of bytes (np.array)\n",
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" \"\"\"\n",
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" txt_str = open(file_name).readlines()\n",
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" del txt_str[-1] #discard last empty line\n",
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" #split each line into bytes and convert from hex\n",
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" txt_bytes_list = list(\n",
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" map(lambda line: \n",
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" list(\n",
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" map(lambda s: int(s, 16),\n",
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" line.rstrip().split(\" \"))\n",
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" ),\n",
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" txt_str)\n",
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" )\n",
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" return np.array(txt_bytes_list, 'uint8')"
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]
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},
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{
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"cell_type": "code",
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2024-01-12 11:29:39 +00:00
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"execution_count": 3,
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2023-12-19 18:19:01 +00:00
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"metadata": {
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"id": "--PH16eNuz_H"
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},
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"outputs": [],
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"source": [
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"# read plaintext inputs\n",
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"inputs = load_text(\"plaintext.txt\")\n",
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"\n",
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"# read length of one complete trace (number of samples per trace)\n",
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"with open(\"traceLength.txt\", \"r\") as fin:\n",
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" trace_length = int(fin.readline())\n",
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"\n",
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"# trim each trace - select interesting part\n",
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"start = 0\n",
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"len = trace_length # CHANGE to the length of the first round; \n",
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"\n",
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"# read traces from binary file\n",
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"traces = np.fromfile(\"traces.bin\", dtype='uint8') # read as linear array\n",
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"traces = np.reshape(traces, (traces.size // trace_length, trace_length)) # reshape into matrix\n",
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"traces = traces[:, start:len] # select only the interesting part of each trace"
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]
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},
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{
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"cell_type": "code",
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2024-01-12 11:29:39 +00:00
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"execution_count": 4,
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2023-12-19 18:19:01 +00:00
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"metadata": {
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"id": "ZVJ_Tk55u1wu"
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},
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2024-01-12 11:29:39 +00:00
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(100, 16)\n",
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"200000\n",
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"(100, 200000)\n"
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]
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}
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],
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2023-12-19 18:19:01 +00:00
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"source": [
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"print(inputs.shape) # dimensions of inputs\n",
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"print(trace_length)\n",
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"print(traces.shape) # dimensions of matrix of traces"
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]
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},
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{
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"cell_type": "code",
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2024-01-12 11:29:39 +00:00
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"execution_count": 6,
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"metadata": {},
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2023-12-19 18:19:01 +00:00
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"outputs": [],
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"source": [
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2024-01-12 11:29:39 +00:00
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"%matplotlib widget"
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2023-12-19 18:19:01 +00:00
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]
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},
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{
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"cell_type": "code",
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2024-01-12 11:29:39 +00:00
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"execution_count": 7,
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2023-12-19 18:19:01 +00:00
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"metadata": {
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"id": "wDAUVmNOu3BP"
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},
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2024-01-12 11:29:39 +00:00
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "8a25fae7cccb4c078bb6a094f047f388",
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"version_major": 2,
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"version_minor": 0
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},
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"image/png": "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"text/html": [
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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" <img src='data:image/png;base64,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" </div>\n",
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" "
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],
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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2023-12-19 18:19:01 +00:00
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"source": [
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"# Plot one trace\n",
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"fig = plt.figure()\n",
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"plt.plot(traces[0])\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "w6boaqAQvF1G"
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},
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"source": [
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"## **Attack the first key byte**\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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2024-01-12 11:29:39 +00:00
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"execution_count": 8,
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2023-12-19 18:19:01 +00:00
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"metadata": {
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"id": "WaKiOUmbvbQR"
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},
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"outputs": [],
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"source": [
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"# Generate key hypotheses (all possible byte values)\n",
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"keys = np.arange(start=0, stop=256, step=1, dtype='uint8')\n",
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"# Select the first byte of each input block\n",
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"inp = inputs[:, 0]\n",
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"# XOR each data byte with each key\n",
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"xmat = inp[:, np.newaxis] ^ keys"
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]
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},
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{
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"cell_type": "code",
|
2024-01-12 11:29:39 +00:00
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"execution_count": 9,
|
2023-12-19 18:19:01 +00:00
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"metadata": {},
|
2024-01-12 11:29:39 +00:00
|
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"outputs": [
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{
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"name": "stdout",
|
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|
"output_type": "stream",
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"text": [
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"[[ 37 235 140 ... 71 237 252]\n",
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" [134 25 178 ... 142 50 68]\n",
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" [215 215 233 ... 61 22 57]\n",
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" ...\n",
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" [ 18 188 253 ... 68 197 189]\n",
|
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|
" [ 87 194 19 ... 160 8 136]\n",
|
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|
|
" [148 238 68 ... 23 94 218]]\n",
|
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|
"(100, 16)\n",
|
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|
|
"(100,)\n",
|
|
|
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|
"(100, 1)\n",
|
|
|
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|
"(256,)\n",
|
|
|
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|
"(100, 256)\n",
|
|
|
|
|
"[[ 37 36 39 ... 216 219 218]\n",
|
|
|
|
|
" [134 135 132 ... 123 120 121]\n",
|
|
|
|
|
" [215 214 213 ... 42 41 40]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [ 18 19 16 ... 239 236 237]\n",
|
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|
" [ 87 86 85 ... 170 169 168]\n",
|
|
|
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|
" [148 149 150 ... 105 106 107]]\n"
|
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|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
2023-12-19 18:19:01 +00:00
|
|
|
"source": [
|
|
|
|
|
"# Examine the inputs matrix. Does it contain the data from plaintext.txt?\n",
|
|
|
|
|
"print(inputs)\n",
|
|
|
|
|
"# What is the shape of all the operands from the previous cell?\n",
|
|
|
|
|
"print(inputs.shape)\n",
|
|
|
|
|
"print(inp.shape)\n",
|
|
|
|
|
"print(inp[:, np.newaxis].shape)\n",
|
|
|
|
|
"print(keys.shape)\n",
|
|
|
|
|
"print(xmat.shape)\n",
|
|
|
|
|
"# Do you understand the values after the XOR operation? What AES operation do they represent?\n",
|
|
|
|
|
"print(xmat)"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "VrBZd18VwBOH"
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},
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"outputs": [],
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"source": [
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"# Substitute with SBOX all XORed values -- matrix of intermediate values\n",
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"smat = sbox[?]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "4GfR9BU-wT4G"
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},
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"outputs": [],
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"source": [
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"# Compute Hamming Weights -- the matrix of hypothetical power consumption\n",
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"hmat = ?[?]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "J8TTPk-WwjQH"
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},
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"outputs": [],
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"source": [
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"# Compute the correlation matrix -- correlate the hypotheses with measured traces\n",
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"print(hmat.shape)\n",
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"print(traces.shape)\n",
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"corr = correlate(?, ?)\n",
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"# What is the shape and contents of the correlation matrix?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "iOqbuNAKxCvP"
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},
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"outputs": [],
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"source": [
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"# Find the absolute maximum correlation\n",
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"acorr = abs(?)\n",
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"max_acorr = ?.max()\n",
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"(k, j) = np.where(acorr == ?) # find idices of maximum\n",
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"print(\"key: %d time: %d\" % (k[0], j[0]))\n",
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"print(\"key: %1c, %02x\" % (k[0], k[0]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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|
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"source": [
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|
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|
|
"# Plot the correlation traces for the right key byte guess and one wrong key byte guess\n",
|
|
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|
|
"# Do you see the correlation peaks?\n",
|
|
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|
|
"fig = plt.figure()\n",
|
|
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|
|
"plt.plot(?)\n",
|
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|
|
"plt.plot(?)\n",
|
|
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|
|
"plt.show()"
|
|
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|
]
|
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|
},
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{
|
|
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|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {
|
|
|
|
|
"id": "Z62RVYJYzncZ"
|
|
|
|
|
},
|
|
|
|
|
"source": [
|
|
|
|
|
"## **Break all key bytes!**"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"id": "T7HhwO-ezpoQ"
|
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"keys = np.array(range(0, 256))\n",
|
|
|
|
|
"kk = np.zeros(16, dtype='uint8')\n",
|
|
|
|
|
"for i in range(0, 16):\n",
|
|
|
|
|
" inp = inputs[:, ?]\n",
|
|
|
|
|
" ????\n",
|
|
|
|
|
" kk[i] = k\n",
|
|
|
|
|
" print(\"%1c, %02x @ %d\" % (k[0], k[0], j[0]))"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"## **Verify the key on a PT, CT pair!**"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"key_bytes = bytes(kk)\n",
|
|
|
|
|
"outputs = ?"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"# !pip install pycryptodome\n",
|
|
|
|
|
"from Crypto.Cipher import AES\n",
|
|
|
|
|
"cipher = AES.new(key_bytes, AES.MODE_ECB)\n",
|
|
|
|
|
"??"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"colab": {
|
|
|
|
|
"collapsed_sections": [],
|
|
|
|
|
"name": "dpa_student.ipynb",
|
|
|
|
|
"provenance": []
|
|
|
|
|
},
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": "Python 3",
|
|
|
|
|
"name": "python3"
|
|
|
|
|
},
|
|
|
|
|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
|
|
|
|
"version": 3
|
|
|
|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
|
|
|
|
"name": "python",
|
|
|
|
|
"nbconvert_exporter": "python",
|
|
|
|
|
"pygments_lexer": "ipython3",
|
2024-01-12 11:29:39 +00:00
|
|
|
"version": "3.11.6"
|
2023-12-19 18:19:01 +00:00
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 0
|
|
|
|
|
}
|
