ids_gmm.ipynb

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  "metadata": {
    "colab": {
      "name": "cicids_gmm.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP01iYSltZqDHxgyDdyAlHo",
      "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/r-dube/CICIDS/blob/main/cicids_gmm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFWTUL3GCHsG"
      },
      "source": [
        "# Load the top modules that are used in multiple places\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrnTamUHDzxI"
      },
      "source": [
        "# Some global variables to drive the script\n",
        "# data_url is the location of the data\n",
        "# Data is not loaded from a local file\n",
        "# Data is loaded from a prepocessed dataset\n",
        "data_url=\"https://raw.githubusercontent.com/r-dube/CICIDS/main/MachineLearningCVE/processed/small-cicids2017.csv\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYEbuZO0MHyf"
      },
      "source": [
        "# label names (YY) in the data and their\n",
        "# mapping to numerical values\n",
        "label_map = {\n",
        " 'BENIGN' : 0,\n",
        " 'FTP-Patator' : 1,\n",
        " 'SSH-Patator' : 2,\n",
        " 'DoS slowloris' : 3,\n",
        " 'DoS Slowhttptest': 4,\n",
        " 'DoS Hulk' : 5,\n",
        " 'DoS GoldenEye' : 6,\n",
        " 'Heartbleed' : 7,\n",
        " 'Web Attack � Brute Force' : 8,\n",
        " 'Web Attack � XSS' : 8,\n",
        " 'Web Attack � Sql Injection' : 8,\n",
        " 'Infiltration' : 9,\n",
        " 'Bot' : 10,\n",
        " 'PortScan' : 11,\n",
        " 'DDoS' : 12,\n",
        "}\n",
        "\n",
        "num_ids_features = 76\n",
        "num_ids_classes = 13\n",
        "ids_classes = [ 'BENIGN', 'FTP-Patator', 'SSH-Patator', 'DoS slowloris', 'DoS Slowhttptest', 'DoS Hulk', 'DoS GoldenEye', 'Heartbleed', 'Brute Force', 'XSS', 'Sql Injection', 'Infiltration', 'Bot', 'PortScan', 'DDoS',]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usYPD_l1Bimz"
      },
      "source": [
        "# Utility functions used by GMM\n",
        "\n",
        "def ids_load_df_from_csv():\n",
        "    \"\"\"\n",
        "    Load dataframe from csv file\n",
        "    Input:\n",
        "        None\n",
        "    Returns:\n",
        "        None\n",
        "    \"\"\"\n",
        "\n",
        "    df = pd.read_csv(data_url)\n",
        "\n",
        "    # uncomment print statements for debugging if data does not load\n",
        "    # print (\"load Dataframe shape\", df.shape)\n",
        "\n",
        "    return df\n",
        "\n",
        "def ids_gmm_std(df):\n",
        "    \"\"\"\n",
        "    Standardize rhe input dataframe\n",
        "    Input:\n",
        "        Dataframe that has columns of covariates followed by a column of labels\n",
        "    Returns:\n",
        "        X, y as numpy arrays\n",
        "    \"\"\"\n",
        "\n",
        "    from sklearn.model_selection import train_test_split\n",
        "    from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "    numcols = len(df.columns)\n",
        "    print(\"df.shape\", df.shape)\n",
        "\n",
        "    X = df.iloc[:, 0:numcols-1]\n",
        "    y = df.loc[:, 'YY']\n",
        "\n",
        "    scaler = StandardScaler()\n",
        "    X = scaler.fit_transform(X)\n",
        "    y = y.values\n",
        "\n",
        "    print (\"X.shape\", X.shape, \"y.shape\", y.shape)\n",
        "\n",
        "    return X, y\n",
        "\n",
        "def ids_gmm_count(y_actual, y_pred):\n",
        "    \"\"\"\n",
        "    \"\"\"\n",
        "    c_a = np.sort(np.bincount(y_actual))\n",
        "    c_a = c_a[c_a != 0]\n",
        "    c_p = np.sort(np.bincount(y_pred))\n",
        "    c_p = c_p[c_p != 0]\n",
        "    print(c_a)\n",
        "    print(c_p)\n",
        "\n",
        "def ids_gmm_accuracy (y_actual, y_pred):\n",
        "    \"\"\"\n",
        "    Input:\n",
        "        Numpy arrays with actual and predicted labels\n",
        "    Returns:\n",
        "        multiclass accuracy and f1 scores\n",
        "    \"\"\"\n",
        "\n",
        "    from sklearn.metrics import accuracy_score\n",
        "    from sklearn.metrics import f1_score\n",
        "\n",
        "    acc = accuracy_score (y_actual, y_pred)\n",
        "    f1 = f1_score(y_actual, y_pred, average='macro')\n",
        "    \n",
        "    return acc, f1"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xi5c64xEcOZ",
        "outputId": "a0ace93b-4146-4922-891c-8d2b8ba7a9a6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Clustering using GMM\n",
        "from sklearn import mixture\n",
        "\n",
        "df = ids_load_df_from_csv ()\n",
        "X, y = ids_gmm_std(df)\n",
        "\n",
        "results_df = pd.DataFrame(columns=['run', 'components','seed', 'BIC'])\n",
        "maxrun = 5\n",
        "maxcomp = 25\n",
        "\n",
        "for run in range (maxrun):\n",
        "  for i in range (1, maxcomp + 1):\n",
        "    seed = 42 + run\n",
        "    clf = mixture.GaussianMixture(n_components=i, covariance_type='full', random_state=seed)\n",
        "    clf.fit(X)\n",
        "    bic = clf.bic(X)\n",
        "    index = run * maxcomp + i\n",
        "    results_df.loc[index, ['run']] = run\n",
        "    results_df.loc[index, ['components']] = i\n",
        "    results_df.loc[index, ['seed']] = seed\n",
        "    results_df.loc[index, ['BIC']] = bic\n",
        "    print (\"run\", run, \"i\", i, \"BIC\", bic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "df.shape (39616, 77)\n",
            "X.shape (39616, 76) y.shape (39616,)\n",
            "run 0 i 1 BIC -12160060.530859668\n",
            "run 0 i 2 BIC -16580730.789570333\n",
            "run 0 i 3 BIC -18738155.91139818\n",
            "run 0 i 4 BIC -21002213.494001534\n",
            "run 0 i 5 BIC -21846982.27958644\n",
            "run 0 i 6 BIC -22868983.48832415\n",
            "run 0 i 7 BIC -25100810.779673476\n",
            "run 0 i 8 BIC -24818595.85774242\n",
            "run 0 i 9 BIC -24806149.405825708\n",
            "run 0 i 10 BIC -26006798.978197146\n",
            "run 0 i 11 BIC -26772894.44680431\n",
            "run 0 i 12 BIC -27056864.951428063\n",
            "run 0 i 13 BIC -27279420.469928805\n",
            "run 0 i 14 BIC -27048134.47678186\n",
            "run 0 i 15 BIC -28542204.976923145\n",
            "run 0 i 16 BIC -28717206.72260838\n",
            "run 0 i 17 BIC -28915295.793504853\n",
            "run 0 i 18 BIC -29007024.341821596\n",
            "run 0 i 19 BIC -29058055.56321362\n",
            "run 0 i 20 BIC -29165762.29537619\n",
            "run 0 i 21 BIC -29082600.43281226\n",
            "run 0 i 22 BIC -29055835.30158223\n",
            "run 0 i 23 BIC -29234193.61366384\n",
            "run 0 i 24 BIC -29176868.491083745\n",
            "run 0 i 25 BIC -29238027.3668958\n",
            "run 1 i 1 BIC -12160060.530859668\n",
            "run 1 i 2 BIC -14893537.130470363\n",
            "run 1 i 3 BIC -18489653.920539297\n",
            "run 1 i 4 BIC -18673480.420734547\n",
            "run 1 i 5 BIC -22634402.259328134\n",
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            "run 1 i 7 BIC -24824907.153084178\n",
            "run 1 i 8 BIC -25099870.101267643\n",
            "run 1 i 9 BIC -25817997.25792267\n",
            "run 1 i 10 BIC -26158937.10222088\n",
            "run 1 i 11 BIC -26467886.35494283\n",
            "run 1 i 12 BIC -26402332.825388893\n",
            "run 1 i 13 BIC -26295153.63078094\n",
            "run 1 i 14 BIC -27312496.413680065\n",
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            "run 1 i 16 BIC -27510602.14660948\n",
            "run 1 i 17 BIC -27656448.067278914\n",
            "run 1 i 18 BIC -28002723.75181946\n",
            "run 1 i 19 BIC -28173640.99286377\n",
            "run 1 i 20 BIC -28218757.367149234\n",
            "run 1 i 21 BIC -28873015.043320775\n",
            "run 1 i 22 BIC -28911371.196911708\n",
            "run 1 i 23 BIC -29030864.30793863\n",
            "run 1 i 24 BIC -28974203.650212195\n",
            "run 1 i 25 BIC -29046231.65375364\n",
            "run 2 i 1 BIC -12160060.530859668\n",
            "run 2 i 2 BIC -16580730.789570333\n",
            "run 2 i 3 BIC -20598100.543108024\n",
            "run 2 i 4 BIC -21632214.119589124\n",
            "run 2 i 5 BIC -21842916.66021067\n",
            "run 2 i 6 BIC -22118083.792094383\n",
            "run 2 i 7 BIC -22082310.626184616\n",
            "run 2 i 8 BIC -25112275.109277934\n",
            "run 2 i 9 BIC -25492010.178250507\n",
            "run 2 i 10 BIC -26138187.79526071\n",
            "run 2 i 11 BIC -26980434.889991675\n",
            "run 2 i 12 BIC -27095279.957562894\n",
            "run 2 i 13 BIC -27025001.13529793\n",
            "run 2 i 14 BIC -27706902.645014003\n",
            "run 2 i 15 BIC -27974672.820124973\n",
            "run 2 i 16 BIC -28181589.75197533\n",
            "run 2 i 17 BIC -28267102.908941686\n",
            "run 2 i 18 BIC -28407123.12770086\n",
            "run 2 i 19 BIC -28480317.334440168\n",
            "run 2 i 20 BIC -28890663.008735992\n",
            "run 2 i 21 BIC -28933568.300288323\n",
            "run 2 i 22 BIC -29003671.929890607\n",
            "run 2 i 23 BIC -29138649.922642443\n",
            "run 2 i 24 BIC -29114904.925500676\n",
            "run 2 i 25 BIC -29221557.542057727\n",
            "run 3 i 1 BIC -12160060.530859668\n",
            "run 3 i 2 BIC -17104566.15671232\n",
            "run 3 i 3 BIC -20828507.056415845\n",
            "run 3 i 4 BIC -21002213.494131804\n",
            "run 3 i 5 BIC -23679757.221704256\n",
            "run 3 i 6 BIC -24858352.255489927\n",
            "run 3 i 7 BIC -25024926.770589054\n",
            "run 3 i 8 BIC -23123205.92591177\n",
            "run 3 i 9 BIC -24349873.74955359\n",
            "run 3 i 10 BIC -24439187.754793305\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-CSy9CBXQAG",
        "outputId": "88518204-5f20-4108-82b8-eb6f83d3363f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(results_df)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    run components seed          BIC\n",
            "1     0          1   42 -1.21601e+07\n",
            "2     0          2   42 -1.65807e+07\n",
            "3     0          3   42 -1.87382e+07\n",
            "4     0          4   42 -2.10022e+07\n",
            "5     0          5   42  -2.1847e+07\n",
            "..   ..        ...  ...          ...\n",
            "121   4         21   46 -2.89935e+07\n",
            "122   4         22   46 -2.89618e+07\n",
            "123   4         23   46  -2.9005e+07\n",
            "124   4         24   46 -2.89741e+07\n",
            "125   4         25   46 -2.89071e+07\n",
            "\n",
            "[125 rows x 4 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFgb-exjGrtl"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn-whitegrid')"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVKWuP5AXi5b",
        "outputId": "61009c22-f36c-43da-8374-fb5548931c00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "colors = ('black', 'blue', 'red', 'green', 'orange')\n",
        "for iter in range (1, 6):\n",
        "  low = (iter - 1) * maxcomp\n",
        "  high = (iter) * maxcomp\n",
        "  x = results_df[low:high].components.values\n",
        "  y = results_df[low:high].BIC.values\n",
        "  plt.xlabel(\"Number of components\")\n",
        "  plt.ylabel(\"Bayesian Information Criteria\")\n",
        "  plt.plot(x, y, '-', color=colors[iter-1])"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}