IT496_ViktoriiaO_Assigment5_Security_Risk_Map.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IT496_ViktoriiaO_Assigment5_Security_Risk Map.ipynb",
      "provenance": [],
      "mount_file_id": "1K2wZ8vnneHfe4InYFElXIq4xR-SHe2Mv",
      "authorship_tag": "ABX9TyOXppNDTSwZ9r5A0CZegMdD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vovsiienko/IT496_Course_acitvities/blob/master/IT496_ViktoriiaO_Assigment5_Security_Risk_Map.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "UZxPxYgPv4Fh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46b531d0-5e46-46a2-ea40-ede5f57fcba3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                Risk  Probability  Impact  Score\n",
            "0  Ransomware attack          0.4      10    4.0\n",
            "1          Data Leak          0.5       9    4.5\n",
            "2        DDoS Attack          0.2       8    1.6\n",
            "3           Phishing          0.3       7    2.1\n",
            "4     Insider threat          0.1       6    0.6\n"
          ]
        }
      ],
      "source": [
        "#import a few libraries\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as plt\n",
        "\n",
        "#Create a dataframe (data table)\n",
        "\n",
        "securityRisksDF=pd.read_csv(\"/content/drive/MyDrive/IT496/SecurityRisks.csv\")\n",
        "#securityRisksDF = securityRisksDF.sort_values(by='Score', ascending=False)\n",
        "print(securityRisksDF)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "securityRisksDF = pd.DataFrame(columns=['Risk', 'Probability', 'Impact', 'Score'])\n",
        "securityRisksDF['Risk']=['Ransomware attack', 'Data leak', 'DDoS attack', 'Phishing', 'Insider threat']\n",
        "securityRisksDF['Probability']=[0.4,0.5,0.2,0.3,0.1]\n",
        "securityRisksDF['Impact']=[10,9,8,7,6]\n",
        "securityRisksDF['Score']=securityRisksDF['Impact']*securityRisksDF['Probability']\n",
        "\n",
        "scoresPivot=securityRisksDF.pivot('Impact', 'Probability', 'Score')\n",
        "#print(scoresPivot)\n",
        "labelsPivot=securityRisksDF.pivot('Impact', 'Probability','Risk')\n",
        "#print(labelsPivot)\n"
      ],
      "metadata": {
        "id": "DSBAUH3zzh7d"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#simple heatmap\n",
        "sns.set(rc={'figure.figsize':(14,7)})\n",
        "p1=sns.heatmap(scoresPivot, cmap=\"flare\", annot=labelsPivot, fmt='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "5hwmDZgH1I3i",
        "outputId": "2a0c24b3-ac7e-4f7e-a8a0-7d78e90de10a"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}