dataset2/dataset2_key.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "2h3iH60GSgCr"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "aYPAVyfGSjVM"
   },
   "outputs": [],
   "source": [
    "start_time = time.time()\n",
    "cleartext = []\n",
    "\n",
    "base_path_python = './'\n",
    "\n",
    "with open(base_path_python + '/cleartext.txt') as csvfile:\n",
    "    reader = csv.reader(csvfile, delimiter=' ')\n",
    "    for element in reader:\n",
    "        cleartext.append(element)\n",
    "cleartext = [[int(num) for num in sublist if num != ''] for sublist in cleartext]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "fgOPlsZ2SniT"
   },
   "outputs": [],
   "source": [
    "# https://femionewin.medium.com/aes-encryption-with-python-step-by-step-3e3ab0b0fd6c\n",
    "sbox = [\n",
    "    0x63, 0x7c, 0x77, 0x7b, 0xf2, 0x6b, 0x6f, 0xc5, 0x30, 0x01, 0x67, 0x2b, 0xfe, 0xd7, 0xab, 0x76,\n",
    "    0xca, 0x82, 0xc9, 0x7d, 0xfa, 0x59, 0x47, 0xf0, 0xad, 0xd4, 0xa2, 0xaf, 0x9c, 0xa4, 0x72, 0xc0,\n",
    "    0xb7, 0xfd, 0x93, 0x26, 0x36, 0x3f, 0xf7, 0xcc, 0x34, 0xa5, 0xe5, 0xf1, 0x71, 0xd8, 0x31, 0x15,\n",
    "    0x04, 0xc7, 0x23, 0xc3, 0x18, 0x96, 0x05, 0x9a, 0x07, 0x12, 0x80, 0xe2, 0xeb, 0x27, 0xb2, 0x75,\n",
    "    0x09, 0x83, 0x2c, 0x1a, 0x1b, 0x6e, 0x5a, 0xa0, 0x52, 0x3b, 0xd6, 0xb3, 0x29, 0xe3, 0x2f, 0x84,\n",
    "    0x53, 0xd1, 0x00, 0xed, 0x20, 0xfc, 0xb1, 0x5b, 0x6a, 0xcb, 0xbe, 0x39, 0x4a, 0x4c, 0x58, 0xcf,\n",
    "    0xd0, 0xef, 0xaa, 0xfb, 0x43, 0x4d, 0x33, 0x85, 0x45, 0xf9, 0x02, 0x7f, 0x50, 0x3c, 0x9f, 0xa8,\n",
    "    0x51, 0xa3, 0x40, 0x8f, 0x92, 0x9d, 0x38, 0xf5, 0xbc, 0xb6, 0xda, 0x21, 0x10, 0xff, 0xf3, 0xd2,\n",
    "    0xcd, 0x0c, 0x13, 0xec, 0x5f, 0x97, 0x44, 0x17, 0xc4, 0xa7, 0x7e, 0x3d, 0x64, 0x5d, 0x19, 0x73,\n",
    "    0x60, 0x81, 0x4f, 0xdc, 0x22, 0x2a, 0x90, 0x88, 0x46, 0xee, 0xb8, 0x14, 0xde, 0x5e, 0x0b, 0xdb,\n",
    "    0xe0, 0x32, 0x3a, 0x0a, 0x49, 0x06, 0x24, 0x5c, 0xc2, 0xd3, 0xac, 0x62, 0x91, 0x95, 0xe4, 0x79,\n",
    "    0xe7, 0xc8, 0x37, 0x6d, 0x8d, 0xd5, 0x4e, 0xa9, 0x6c, 0x56, 0xf4, 0xea, 0x65, 0x7a, 0xae, 0x08,\n",
    "    0xba, 0x78, 0x25, 0x2e, 0x1c, 0xa6, 0xb4, 0xc6, 0xe8, 0xdd, 0x74, 0x1f, 0x4b, 0xbd, 0x8b, 0x8a,\n",
    "    0x70, 0x3e, 0xb5, 0x66, 0x48, 0x03, 0xf6, 0x0e, 0x61, 0x35, 0x57, 0xb9, 0x86, 0xc1, 0x1d, 0x9e,\n",
    "    0xe1, 0xf8, 0x98, 0x11, 0x69, 0xd9, 0x8e, 0x94, 0x9b, 0x1e, 0x87, 0xe9, 0xce, 0x55, 0x28, 0xdf,\n",
    "    0x8c, 0xa1, 0x89, 0x0d, 0xbf, 0xe6, 0x42, 0x68, 0x41, 0x99, 0x2d, 0x0f, 0xb0, 0x54, 0xbb, 0x16]\n",
    "\n",
    "model = []\n",
    "for x in range(150): # For all the cleartexts\n",
    "    trace = []\n",
    "    for y in range(256): # For all the keys\n",
    "        for z in range(16): # For all the positions\n",
    "            xor = (cleartext[x][z])^y\n",
    "            HW = bin(sbox[xor]).count('1') # SBOX + count the number of 1s (Hamming weight)\n",
    "        trace.append(HW)\n",
    "    model.append(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "id": "O78nATqpUx6Y",
    "outputId": "1feb12bd-c2f5-4144-b611-337c1b134ebb"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(1,257,1), model[0])\n",
    "plt.xlabel('Sample')\n",
    "plt.ylabel('Power consumption')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HkS55rhoTiu3",
    "outputId": "1896f7ad-0411-4917-a569-f9cb6b6328c8",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing byte 0\n",
      "   The length is 4960\n",
      "       Potential match at k = 2944 with key = 84 and correlation of 0.6120983299965751\n",
      "       Potential match at k = 2945 with key = 84 and correlation of 0.6970573982138719\n",
      "       Potential match at k = 2946 with key = 84 and correlation of 0.7062721790879665\n",
      "       Potential match at k = 2947 with key = 84 and correlation of 0.6528033430759514\n",
      "       Potential match at k = 2948 with key = 84 and correlation of 0.6549074412418301\n",
      "Analyzing byte 1\n",
      "   The length is 4970\n",
      "       Potential match at k = 2944 with key = 104 and correlation of 0.6600602295556516\n",
      "       Potential match at k = 2945 with key = 104 and correlation of 0.6968065256877806\n",
      "       Potential match at k = 2946 with key = 104 and correlation of 0.6395281732605222\n",
      "       Potential match at k = 3224 with key = 104 and correlation of 0.611889744675869\n",
      "       Potential match at k = 3225 with key = 104 and correlation of 0.7116819104897221\n",
      "       Potential match at k = 3226 with key = 104 and correlation of 0.7102102934308444\n",
      "       Potential match at k = 3227 with key = 104 and correlation of 0.6742157200473968\n",
      "       Potential match at k = 3228 with key = 104 and correlation of 0.650122414446453\n",
      "Analyzing byte 2\n",
      "   The length is 4970\n",
      "       Potential match at k = 2944 with key = 97 and correlation of 0.7080832542446579\n",
      "       Potential match at k = 2945 with key = 97 and correlation of 0.7728003142155809\n",
      "       Potential match at k = 2946 with key = 97 and correlation of 0.721305932374873\n",
      "       Potential match at k = 2947 with key = 97 and correlation of 0.6737056234675292\n",
      "       Potential match at k = 2948 with key = 97 and correlation of 0.6602744730239674\n",
      "       Potential match at k = 2949 with key = 97 and correlation of 0.640082401068125\n",
      "       Potential match at k = 3224 with key = 97 and correlation of 0.6474041469476333\n",
      "       Potential match at k = 3225 with key = 97 and correlation of 0.7367219727320874\n",
      "       Potential match at k = 3226 with key = 97 and correlation of 0.7278282739041925\n",
      "       Potential match at k = 3227 with key = 97 and correlation of 0.7488602009202237\n",
      "       Potential match at k = 3228 with key = 97 and correlation of 0.7457763931315641\n",
      "       Potential match at k = 3229 with key = 97 and correlation of 0.7268262214589006\n",
      "       Potential match at k = 4663 with key = 97 and correlation of 0.6196439237059091\n",
      "Analyzing byte 3\n",
      "   The length is 4970\n",
      "       Potential match at k = 2945 with key = 116 and correlation of 0.7321866694617528\n",
      "       Potential match at k = 2946 with key = 116 and correlation of 0.7126724871611271\n",
      "       Potential match at k = 2947 with key = 116 and correlation of 0.6435367577922971\n",
      "       Potential match at k = 2949 with key = 116 and correlation of 0.6229294930971485\n",
      "       Potential match at k = 3227 with key = 116 and correlation of 0.6125410211596758\n",
      "       Potential match at k = 4664 with key = 116 and correlation of 0.6214132442999738\n",
      "       Potential match at k = 4665 with key = 116 and correlation of 0.6203395989424136\n",
      "       Potential match at k = 4666 with key = 116 and correlation of 0.606779787572889\n",
      "Analyzing byte 4\n",
      "   The length is 4970\n",
      "       Potential match at k = 2944 with key = 115 and correlation of 0.6605719449835615\n",
      "       Potential match at k = 2945 with key = 115 and correlation of 0.6893716881765173\n",
      "       Potential match at k = 2946 with key = 115 and correlation of 0.6800530329691573\n",
      "       Potential match at k = 2947 with key = 115 and correlation of 0.6260536308434624\n",
      "       Potential match at k = 3225 with key = 115 and correlation of 0.6565580451387079\n",
      "       Potential match at k = 3226 with key = 115 and correlation of 0.6255940029899305\n",
      "       Potential match at k = 3227 with key = 115 and correlation of 0.6120592980170726\n",
      "       Potential match at k = 3228 with key = 115 and correlation of 0.6200881771311326\n",
      "       Potential match at k = 4664 with key = 115 and correlation of 0.6705017941413183\n",
      "       Potential match at k = 4665 with key = 115 and correlation of 0.6764767031395683\n",
      "       Potential match at k = 4666 with key = 115 and correlation of 0.6270790156911799\n",
      "       Potential match at k = 4669 with key = 115 and correlation of 0.601823301002059\n",
      "Analyzing byte 5\n",
      "   The length is 4960\n",
      "       Potential match at k = 2944 with key = 32 and correlation of 0.6802818607313408\n",
      "       Potential match at k = 2945 with key = 32 and correlation of 0.7171967679317289\n",
      "       Potential match at k = 2946 with key = 32 and correlation of 0.7241060794434405\n",
      "       Potential match at k = 2947 with key = 32 and correlation of 0.7188741700120869\n",
      "       Potential match at k = 2948 with key = 32 and correlation of 0.680861477646883\n",
      "       Potential match at k = 2949 with key = 32 and correlation of 0.6279805681316987\n",
      "       Potential match at k = 3224 with key = 32 and correlation of 0.628213664121753\n",
      "       Potential match at k = 3225 with key = 32 and correlation of 0.7472151754315156\n",
      "       Potential match at k = 3226 with key = 32 and correlation of 0.737225901022138\n",
      "       Potential match at k = 3227 with key = 32 and correlation of 0.7121251813730947\n",
      "       Potential match at k = 3228 with key = 32 and correlation of 0.7078582295280617\n",
      "       Potential match at k = 3229 with key = 32 and correlation of 0.6727080936384441\n",
      "       Potential match at k = 3255 with key = 32 and correlation of 0.6259061108310774\n",
      "       Potential match at k = 3256 with key = 32 and correlation of 0.6276766021283948\n",
      "       Potential match at k = 3257 with key = 32 and correlation of 0.6189593389570771\n",
      "       Potential match at k = 3258 with key = 32 and correlation of 0.6137541190948886\n",
      "       Potential match at k = 4663 with key = 32 and correlation of 0.6027843259939248\n",
      "       Potential match at k = 4664 with key = 32 and correlation of 0.6741424899432608\n",
      "       Potential match at k = 4665 with key = 32 and correlation of 0.6306430244742585\n",
      "       Potential match at k = 4666 with key = 32 and correlation of 0.6199683954786687\n",
      "       Potential match at k = 4667 with key = 32 and correlation of 0.6178285502964376\n",
      "       Potential match at k = 4668 with key = 32 and correlation of 0.6101284101651159\n",
      "Analyzing byte 6\n",
      "   The length is 4970\n",
      "       Potential match at k = 2945 with key = 109 and correlation of 0.6889832523456021\n",
      "       Potential match at k = 2946 with key = 109 and correlation of 0.7050733778354225\n",
      "       Potential match at k = 2947 with key = 109 and correlation of 0.6797829519060297\n",
      "       Potential match at k = 2948 with key = 109 and correlation of 0.6382518000605678\n",
      "Analyzing byte 7\n",
      "   The length is 4970\n",
      "       Potential match at k = 2945 with key = 121 and correlation of 0.6811106344904887\n",
      "       Potential match at k = 2946 with key = 121 and correlation of 0.640887660203187\n",
      "       Potential match at k = 2947 with key = 121 and correlation of 0.608014307646828\n",
      "       Potential match at k = 2948 with key = 121 and correlation of 0.6080024183013611\n",
      "Analyzing byte 8\n",
      "   The length is 4960\n",
      "       Potential match at k = 2944 with key = 32 and correlation of 0.6724634054752422\n",
      "       Potential match at k = 2945 with key = 32 and correlation of 0.7188120880102972\n",
      "       Potential match at k = 2946 with key = 32 and correlation of 0.7077465004202551\n",
      "       Potential match at k = 2947 with key = 32 and correlation of 0.6339901556560896\n",
      "       Potential match at k = 3226 with key = 32 and correlation of 0.6067730354657361\n",
      "       Potential match at k = 3227 with key = 32 and correlation of 0.6287458177314731\n",
      "       Potential match at k = 3228 with key = 32 and correlation of 0.6218594346143573\n",
      "       Potential match at k = 3229 with key = 32 and correlation of 0.6384576789814891\n",
      "       Potential match at k = 4666 with key = 32 and correlation of 0.6170097270027901\n",
      "Analyzing byte 9\n",
      "   The length is 4970\n",
      "       Potential match at k = 2944 with key = 75 and correlation of 0.6112886693969838\n",
      "       Potential match at k = 2945 with key = 75 and correlation of 0.7156198788112924\n",
      "       Potential match at k = 2946 with key = 75 and correlation of 0.6852881605398091\n",
      "       Potential match at k = 2947 with key = 75 and correlation of 0.6276130729308333\n",
      "       Potential match at k = 2948 with key = 75 and correlation of 0.6398586920557069\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Potential match at k = 2949 with key = 75 and correlation of 0.6116554301895054\n",
      "       Potential match at k = 3227 with key = 75 and correlation of 0.6175161504933057\n",
      "       Potential match at k = 3228 with key = 75 and correlation of 0.6065888210187655\n",
      "Analyzing byte 10\n",
      "   The length is 4960\n",
      "       Potential match at k = 2945 with key = 117 and correlation of 0.6658156085003922\n",
      "       Potential match at k = 2946 with key = 117 and correlation of 0.6544645958520494\n",
      "       Potential match at k = 2947 with key = 117 and correlation of 0.6322359095687877\n",
      "       Potential match at k = 4666 with key = 117 and correlation of 0.6097264051034116\n",
      "       Potential match at k = 4667 with key = 117 and correlation of 0.6234380651711023\n",
      "Analyzing byte 11\n",
      "   The length is 4970\n",
      "       Potential match at k = 2945 with key = 110 and correlation of 0.6474461187452973\n",
      "       Potential match at k = 2946 with key = 110 and correlation of 0.673842119005473\n",
      "       Potential match at k = 2947 with key = 110 and correlation of 0.6561120334026289\n",
      "       Potential match at k = 2948 with key = 110 and correlation of 0.6069706062092464\n",
      "Analyzing byte 12\n",
      "   The length is 4970\n",
      "       Potential match at k = 2944 with key = 103 and correlation of 0.7494274807869005\n",
      "       Potential match at k = 2945 with key = 103 and correlation of 0.7314439324486199\n",
      "       Potential match at k = 2946 with key = 103 and correlation of 0.7378849889491321\n",
      "       Potential match at k = 2947 with key = 103 and correlation of 0.7300132974724305\n",
      "       Potential match at k = 2948 with key = 103 and correlation of 0.7085059946777604\n",
      "       Potential match at k = 2949 with key = 103 and correlation of 0.6460517101821408\n",
      "       Potential match at k = 3225 with key = 103 and correlation of 0.6619967489311899\n",
      "       Potential match at k = 3226 with key = 103 and correlation of 0.679418221087186\n",
      "       Potential match at k = 3227 with key = 103 and correlation of 0.6672155591091183\n",
      "       Potential match at k = 3228 with key = 103 and correlation of 0.6515859703778305\n",
      "       Potential match at k = 3229 with key = 103 and correlation of 0.6746206076430733\n",
      "       Potential match at k = 4664 with key = 103 and correlation of 0.6303535874411137\n",
      "       Potential match at k = 4665 with key = 103 and correlation of 0.6075252239357551\n",
      "       Potential match at k = 4668 with key = 103 and correlation of 0.6195805267451185\n",
      "Analyzing byte 13\n",
      "   The length is 4970\n",
      "       Potential match at k = 2944 with key = 32 and correlation of 0.652360481461299\n",
      "       Potential match at k = 2945 with key = 32 and correlation of 0.6869972752995799\n",
      "       Potential match at k = 2946 with key = 32 and correlation of 0.721222218335213\n",
      "       Potential match at k = 2947 with key = 32 and correlation of 0.7304844527563891\n",
      "       Potential match at k = 2948 with key = 32 and correlation of 0.682750981277587\n",
      "       Potential match at k = 2949 with key = 32 and correlation of 0.6596063195445434\n",
      "       Potential match at k = 3226 with key = 32 and correlation of 0.6027946574765651\n",
      "       Potential match at k = 3229 with key = 32 and correlation of 0.6043714051565826\n",
      "Analyzing byte 14\n",
      "   The length is 4960\n",
      "       Potential match at k = 2944 with key = 70 and correlation of 0.6994957827425677\n",
      "       Potential match at k = 2945 with key = 70 and correlation of 0.7422534203890374\n",
      "       Potential match at k = 2946 with key = 70 and correlation of 0.7590408879804229\n",
      "       Potential match at k = 2947 with key = 70 and correlation of 0.7630888182708504\n",
      "       Potential match at k = 2948 with key = 70 and correlation of 0.7326541637771842\n",
      "       Potential match at k = 2949 with key = 70 and correlation of 0.6812425583541077\n",
      "       Potential match at k = 3225 with key = 70 and correlation of 0.6503748447889734\n",
      "       Potential match at k = 3226 with key = 70 and correlation of 0.6413559478348756\n",
      "       Potential match at k = 3227 with key = 70 and correlation of 0.6391943733573634\n",
      "       Potential match at k = 3228 with key = 70 and correlation of 0.6585393369770862\n",
      "       Potential match at k = 3229 with key = 70 and correlation of 0.6353905575675086\n",
      "       Potential match at k = 4664 with key = 70 and correlation of 0.669798751828756\n",
      "       Potential match at k = 4665 with key = 70 and correlation of 0.6202933692121304\n",
      "       Potential match at k = 4666 with key = 70 and correlation of 0.6003886204289044\n",
      "       Potential match at k = 4667 with key = 70 and correlation of 0.6004749673880299\n",
      "       Potential match at k = 4668 with key = 70 and correlation of 0.603799710429923\n",
      "       Potential match at k = 4669 with key = 70 and correlation of 0.6029452925435641\n",
      "Analyzing byte 15\n",
      "   The length is 4960\n",
      "       Potential match at k = 2945 with key = 117 and correlation of 0.6141919387216099\n",
      "       Potential match at k = 2946 with key = 117 and correlation of 0.6019572503587031\n",
      "The key of dataset2 is  [84, 104, 97, 116, 115, 32, 109, 121, 32, 75, 117, 110, 103, 32, 70, 117]\n"
     ]
    }
   ],
   "source": [
    "key = []\n",
    "limit = 0.8 # We set 0.8 as the value that, once surpassed, it means that we are in a rising flank\n",
    "\n",
    "for z in range(16): # z is the byte of the key we are analyzing\n",
    "    trace_byte = []\n",
    "    clock_byte = []\n",
    "    print(\"Analyzing byte \"+str(z))\n",
    "\n",
    "    with open(base_path_python + '/trace'+str(z)+'.txt') as csvfile:\n",
    "        reader = csv.reader(csvfile, delimiter='\\t')\n",
    "        for element in reader:\n",
    "            trace_byte.append(element)\n",
    "    trace_byte = [[float(num) for num in sublist] for sublist in trace_byte]\n",
    "\n",
    "    trace_flank = []\n",
    "\n",
    "    with open(base_path_python + '/clock'+str(z)+'.txt') as csvfile:\n",
    "        reader = csv.reader(csvfile, delimiter='\\t')\n",
    "        for element in reader:\n",
    "            clock_byte.append(element)\n",
    "    clock_byte = [[float(num) for num in sublist] for sublist in clock_byte]\n",
    "\n",
    "    for i in range(150):\n",
    "        aux = []\n",
    "        for j in range(50000):\n",
    "            if (j > 5 and j < 49995 and clock_byte[i][j-1] < limit and clock_byte[i][j] >= limit):\n",
    "                for x in range(10): # We store the 10 traces for each rising flank\n",
    "                    aux.append(trace_byte[i][j-5+x])\n",
    "        trace_flank.append(aux)\n",
    "\n",
    "    model = []\n",
    "\n",
    "    for x in range(150): # For all the cleartexts\n",
    "        trace = []\n",
    "        for y in range(256): # For all the keys\n",
    "            xor = (cleartext[x][z])^(y)\n",
    "            HW = bin(sbox[xor]).count('1') # SBOX + count the number of 1s (Hamming weight)\n",
    "            trace.append(HW)\n",
    "        model.append(trace)\n",
    "\n",
    "    model_transposed = []\n",
    "    for i in range(256):\n",
    "            model_i = []\n",
    "            for j in range(150):\n",
    "                model_i.append(model[j][i])\n",
    "            model_transposed.append(model_i)\n",
    "    \n",
    "    min_length = len(trace_flank[0])\n",
    "    for j in range(150):\n",
    "        length_i = len(trace_flank[j])\n",
    "        if (length_i < min_length):\n",
    "            min_length = length_i\n",
    "    print(\"   The length is \"+str(min_length))\n",
    "    \n",
    "    # CPA\n",
    "    max = 0\n",
    "    key_i = 0\n",
    "    \n",
    "    for k in range(min_length):\n",
    "        corr_i = []\n",
    "        trace_i = []\n",
    "        for j in range(150):\n",
    "            trace_i.append(trace_flank[j][k])\n",
    "\n",
    "        for i in range(256):\n",
    "\n",
    "            corr0 = np.corrcoef(trace_i, model_transposed[i])\n",
    "\n",
    "            value = abs(corr0[0][1])\n",
    "            if (value >= 0.6):\n",
    "                print(\"       Potential match at k = \"+str(k)+\" with key = \"+str(i)+\" and correlation of \"+str(value))\n",
    "                if (value >= max): # We store the value with the highest correlation (i.e. the key)\n",
    "                    max = value\n",
    "                    key_i = i\n",
    "    \n",
    "    key.append(key_i)\n",
    "\n",
    "print(\"The key of dataset2 is \", key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vCiPURVhTiu6",
    "outputId": "bc2242cb-7dcc-447d-dcbb-641a5e11f38f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checksum:  1434\n",
      "Checksum is correct\n"
     ]
    }
   ],
   "source": [
    "checksum = sum(key)\n",
    "print(\"Checksum: \",checksum)\n",
    "if checksum == 1434:\n",
    "    print(\"Checksum is correct\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9TmS_OzMyAXR",
    "outputId": "f1523f57-2d32-4457-c739-e5b3d36247ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 01:06:37\n"
     ]
    }
   ],
   "source": [
    "elapsed_time = time.time() - start_time\n",
    "print('Execution time:', time.strftime(\"%H:%M:%S\", time.gmtime(elapsed_time)))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "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",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}