desvsblowfish(cfb).ipynb

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  "metadata": {
    "colab": {
      "name": "desvsblowfish(cfb).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mattyco/Critical-Analysis-of-DES-and-Blowfish/blob/master/desvsblowfish(cfb).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amOp5Byxesb4",
        "colab_type": "text"
      },
      "source": [
        "#Section 1\n",
        "Blowfish Python"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPMsVEYcKo9A",
        "colab_type": "code",
        "outputId": "b28f40a3-81ca-490d-c18e-c576e99d38e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "pip install pycryptodome"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pycryptodome\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/af/16/da16a22d47bac9bf9db39f3b9af74e8eeed8855c0df96be20b580ef92fff/pycryptodome-3.9.7-cp36-cp36m-manylinux1_x86_64.whl (13.7MB)\n",
            "\u001b[K     |████████████████████████████████| 13.7MB 309kB/s \n",
            "\u001b[?25hInstalling collected packages: pycryptodome\n",
            "Successfully installed pycryptodome-3.9.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XM3u2DflLG-D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from Crypto.Cipher import Blowfish\n",
        "from struct import pack\n",
        "import os, random\n",
        "import binascii\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGip9zv3NWc2",
        "colab_type": "code",
        "outputId": "9d5fb198-6321-40e3-e7ef-62b76dcd98d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjd2x-SWNjra",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path ='/content/drive/My Drive/CompSec/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-JsuO324r8W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bs = Blowfish.block_size\n",
        "bskey = b'01234567'\n",
        "bfobj = Blowfish.new(bskey, Blowfish.MODE_CFB)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaBmUUVpP2ed",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def encrypt_bf_file(bs, key, in_filename, out_filename=None):\n",
        "    if not out_filename:\n",
        "        out_filename = in_filename + '.enc'\n",
        "    size = os.path.getsize(in_filename)\n",
        "    infile = open(in_filename, 'rb')\n",
        "    outfile = open(out_filename, 'wb')\n",
        "    data = infile.read()\n",
        "    infile.close()\n",
        "    if size % bs > 0:  # Add padding if size if not divisible by 8\n",
        "        extra = bs-(size % bs)\n",
        "        padding = [0]*extra\n",
        "        padding = pack('b'*extra, *padding)\n",
        "        data += padding\n",
        "    start_time=time.time()\n",
        "    encrypted_data = bfobj.iv + bfobj.encrypt(data)\n",
        "    end_time=time.time()\n",
        "    outfile.write(encrypted_data)\n",
        "    outfile.close()\n",
        "    return (end_time - start_time)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJspz819PZyV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def decrypt_bf_file(key, in_filename, out_filename):\n",
        "    infile = open(in_filename, 'rb')\n",
        "    outfile = open(out_filename, 'wb')\n",
        "    data = infile.read()\n",
        "    infile.close()\n",
        "    iv = data[:bs]\n",
        "    data = data[bs:]\n",
        "    start_time=time.time()\n",
        "    bfobjnew = Blowfish.new(key, Blowfish.MODE_CFB, iv)\n",
        "    decrypted_data = bfobjnew.decrypt(data)\n",
        "    end_time=time.time()\n",
        "    end = len(decrypted_data) - 1\n",
        "    while binascii.hexlify(bytes(str(decrypted_data[end]), 'utf-8')) == '00':\n",
        "        end -= 1\n",
        "    decrypted_data = decrypted_data[0:end]\n",
        "    outfile.write(decrypted_data)\n",
        "    outfile.close()\n",
        "    return(end_time - start_time)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fwAL-f0KayGq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def findbfavgtime(bs,key,filename):\n",
        "  avg_enc_time=0\n",
        "  avg_dec_time=0\n",
        "  for i in range(10):\n",
        "    time_taken_enc=encrypt_bf_file(bs,key, path + filename)\n",
        "    time_taken_dec=decrypt_bf_file(key, path + filename + \".enc\",path + 'out.txt')\n",
        "    # print(time_taken)\n",
        "    avg_enc_time+=time_taken_enc\n",
        "    avg_dec_time+=time_taken_dec\n",
        "    # time.sleep(2)\n",
        "\n",
        "  avg_enc_time*=100\n",
        "  avg_dec_time*=100\n",
        "  return(avg_enc_time,avg_dec_time)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSXskxhEfCBF",
        "colab_type": "text"
      },
      "source": [
        "#Section 2\n",
        "DES Python\n",
        "Key size 64 bit.\n",
        "ECB Mode (Electronic Code Book)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxRE82K5fPKM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from Crypto.Cipher import DES\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNm-xPtEfdRs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "desbs = DES.block_size\n",
        "deskey=b'01234567'\n",
        "desobj = DES.new(deskey, DES.MODE_CFB)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6C_fErBgaih",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def encrypt_des_file(bs,key, in_filename, out_filename=None):\n",
        "    if not out_filename:\n",
        "        out_filename = in_filename + '.enc'\n",
        "    size = os.path.getsize(in_filename)\n",
        "    infile = open(in_filename, 'rb')\n",
        "    outfile = open(out_filename, 'wb')\n",
        "    data = infile.read()\n",
        "    infile.close()\n",
        "    if size % bs > 0:  # Add padding if size if not divisible by 8\n",
        "        extra = bs-(size % bs)\n",
        "        padding = [0]*extra\n",
        "        padding = pack('b'*extra, *padding)\n",
        "        data += padding\n",
        "    start_time=time.time()\n",
        "    encrypted_data = desobj.iv + desobj.encrypt(data)\n",
        "    end_time=time.time()\n",
        "    outfile.write(encrypted_data)\n",
        "    outfile.close()\n",
        "    return (end_time - start_time)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cm4v14BRgbff",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def decrypt_des_file(key, in_filename, out_filename):\n",
        "    infile = open(in_filename, 'rb')\n",
        "    outfile = open(out_filename, 'wb')\n",
        "    data = infile.read()\n",
        "    infile.close()\n",
        "    iv = data[:desbs]\n",
        "    data = data[desbs:]\n",
        "    start_time=time.time()\n",
        "    desobjnew = DES.new(key, DES.MODE_CFB, iv)\n",
        "    decrypted_data = desobjnew.decrypt(data)\n",
        "    end_time=time.time()\n",
        "    end = len(decrypted_data) - 1\n",
        "    while binascii.hexlify(bytes(str(decrypted_data[end]), 'utf-8')) == '00':\n",
        "        end -= 1\n",
        "    decrypted_data = decrypted_data[0:end]\n",
        "    outfile.write(decrypted_data)\n",
        "    outfile.close()\n",
        "    return(end_time - start_time)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2P3rxp-Jh16",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def finddesavgtime(bs,key,filename):\n",
        "  avg_enc_time=0\n",
        "  avg_dec_time=0\n",
        "  for i in range(10):\n",
        "    time_taken_enc=encrypt_des_file(bs,key, path + filename)\n",
        "    time_taken_dec=decrypt_des_file(key, path + filename + \".enc\",path + 'out.txt')\n",
        "    # print(time_taken)\n",
        "    avg_enc_time+=time_taken_enc\n",
        "    avg_dec_time+=time_taken_dec\n",
        "    # time.sleep(1)\n",
        "\n",
        "  avg_enc_time*=100\n",
        "  avg_dec_time*=100\n",
        "  return(avg_enc_time,avg_dec_time)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aV_OAKQXL-h",
        "colab_type": "text"
      },
      "source": [
        "#Section 3\n",
        "Comparing the values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcOVDS1CXWQK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from prettytable import PrettyTable"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bvTW_lYXjru",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "enc_table=PrettyTable(['File','DES','Blowfish'])\n",
        "dec_table=PrettyTable(['File','DES','Blowfish'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1jJX7YAfkM0M",
        "colab": {}
      },
      "source": [
        "enc_data=[[],[]]\n",
        "dec_data=[[],[]]\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'vid10MB.mp4')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'vid10MB.mp4')\n",
        "enc_table.add_row(['mp4 10MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['mp4 10MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'vid20MB.mp4')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'vid20MB.mp4')\n",
        "enc_table.add_row(['mp4 20MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['mp4 20MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'vid30MB.mp4')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'vid30MB.mp4')\n",
        "enc_table.add_row(['mp4 30MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['mp4 30MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'xml10MB.xml')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'xml10MB.xml')\n",
        "enc_table.add_row(['xml 10MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['xml 10MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'xml20MB.xml')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'xml20MB.xml')\n",
        "enc_table.add_row(['xml 20MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['xml 20MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'xml30MB.xml')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'xml30MB.xml')\n",
        "enc_table.add_row(['xml 30MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['xml 30MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'txt10MB.txt')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'txt10MB.txt')\n",
        "enc_table.add_row(['txt 10MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['txt 10MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'txt20MB.txt')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'txt20MB.txt')\n",
        "enc_table.add_row(['txt 20MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['txt 20MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)\n",
        "\n",
        "des_enc,des_dec=finddesavgtime(desbs,deskey,'txt30MB.txt')\n",
        "bf_enc,bf_dec=findbfavgtime(bs,bskey,'txt30MB.txt')\n",
        "enc_table.add_row(['txt 30MB',des_enc,bf_enc])\n",
        "dec_table.add_row(['txt 30MB',des_dec,bf_dec])\n",
        "enc_data[0].append(des_enc)\n",
        "enc_data[1].append(bf_enc)\n",
        "dec_data[0].append(des_dec)\n",
        "dec_data[1].append(bf_dec)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jp6UJHvaY4c-",
        "colab_type": "code",
        "outputId": "c0d504ca-23d0-4859-a605-f7b04d4392af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "source": [
        "print(\"Encryption times (in ms):\\n\",enc_table)\n",
        "print(\"\\nDecryption times (in ms):\\n\",dec_table)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Encryption times (in ms):\n",
            " +----------+--------------------+--------------------+\n",
            "|   File   |        DES         |      Blowfish      |\n",
            "+----------+--------------------+--------------------+\n",
            "| mp4 10MB | 1315.0088787078857 | 900.7773637771606  |\n",
            "| mp4 20MB | 2625.6743907928467 | 1802.8513431549072 |\n",
            "| mp4 30MB | 3932.0351600646973 | 2774.0725994110107 |\n",
            "| xml 10MB | 1309.1857194900513 | 903.2992839813232  |\n",
            "| xml 20MB | 2620.5193519592285 | 1798.5727548599243 |\n",
            "| xml 30MB | 3929.4090509414673 | 2701.310396194458  |\n",
            "| txt 10MB | 1313.8607501983643 | 900.6488084793091  |\n",
            "| txt 20MB | 2621.663522720337  | 1802.1814823150635 |\n",
            "| txt 30MB | 3925.2537965774536 | 2699.0203619003296 |\n",
            "+----------+--------------------+--------------------+\n",
            "\n",
            "Decryption times (in ms):\n",
            " +----------+--------------------+--------------------+\n",
            "|   File   |        DES         |      Blowfish      |\n",
            "+----------+--------------------+--------------------+\n",
            "| mp4 10MB | 1312.715220451355  |  907.391619682312  |\n",
            "| mp4 20MB | 2618.3655738830566 | 1802.4669408798218 |\n",
            "| mp4 30MB | 3931.928777694702  | 2748.448586463928  |\n",
            "| xml 10MB | 1312.5750064849854 |  896.568775177002  |\n",
            "| xml 20MB | 2617.9049253463745 | 1793.4605121612549 |\n",
            "| xml 30MB | 3928.488874435425  | 2690.5388355255127 |\n",
            "| txt 10MB | 1309.0013027191162 | 895.4428195953369  |\n",
            "| txt 20MB | 2621.652412414551  | 1807.526159286499  |\n",
            "| txt 30MB | 3925.6356239318848 | 2688.8675689697266 |\n",
            "+----------+--------------------+--------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vn94r55RyQd9",
        "colab_type": "text"
      },
      "source": [
        "#Section 4 : Visualisation\n",
        "using matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxTZhVfcyHE-",
        "colab_type": "code",
        "outputId": "2f4ffbcd-15a1-4470-973b-cad9e89da3f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "print(enc_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1315.0088787078857, 2625.6743907928467, 3932.0351600646973, 1309.1857194900513, 2620.5193519592285, 3929.4090509414673, 1313.8607501983643, 2621.663522720337, 3925.2537965774536], [900.7773637771606, 1802.8513431549072, 2774.0725994110107, 903.2992839813232, 1798.5727548599243, 2701.310396194458, 900.6488084793091, 1802.1814823150635, 2699.0203619003296]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxBOyb_4yHB3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdNMSJXHyrCV",
        "colab_type": "code",
        "outputId": "ace39311-7f83-4433-c47a-bdbef2de8feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "#create encryption bar graph\n",
        "index=np.arange(9)\n",
        "fig = plt.figure()\n",
        "bar_width = 0.35\n",
        "opacity = 0.8\n",
        "rects1 = plt.barh(index, enc_data[0], bar_width,\n",
        "alpha=opacity,\n",
        "color='b',label=\"DES\")\n",
        "\n",
        "rects1 = plt.barh(index+bar_width, enc_data[1], bar_width,\n",
        "alpha=opacity,\n",
        "color='r',label=\"Blowfish\")\n",
        "\n",
        "plt.ylabel('File Type (with size)')\n",
        "plt.xlabel('Encryption Time (in milliseconds)')\n",
        "plt.title('Encryption Time based on File Type and Size')\n",
        "plt.yticks(index + bar_width, ('MP4 10MB', 'MP4 20MB', 'MP4 30MB', 'XML 10MB','XML 20MB','XML 30MB','TXT 10MB','TXT 20MB','TXT 30MB'))\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORgdie_Rs8r-",
        "colab_type": "code",
        "outputId": "cb63d70e-f103-48ce-948c-bc83f27b5c4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "#create decryption bar graph\n",
        "index=np.arange(9)\n",
        "fig = plt.figure()\n",
        "bar_width = 0.35\n",
        "opacity = 0.8\n",
        "rects1 = plt.barh(index, dec_data[0], bar_width,\n",
        "alpha=opacity,\n",
        "color='g',label=\"DES\")\n",
        "\n",
        "rects1 = plt.barh(index+bar_width, dec_data[1], bar_width,\n",
        "alpha=opacity,\n",
        "color='black',label=\"Blowfish\")\n",
        "\n",
        "plt.ylabel('File Type (with size)')\n",
        "plt.xlabel('Decryption Time (in milliseconds)')\n",
        "plt.title('Decryption Time based on File Type and Size')\n",
        "plt.yticks(index + bar_width, ('MP4 10MB', 'MP4 20MB', 'MP4 30MB', 'XML 10MB','XML 20MB','XML 30MB','TXT 10MB','TXT 20MB','TXT 30MB'))\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}