Bruteforce_Detection.ipynb

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        "WYGFJ_dNqQGY"
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      "authorship_tag": "ABX9TyM5w2ouvQPmbTIgd+57fSZD",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU",
    "gpuClass": "premium"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/manny-uncharted/Deep-learning-projects/blob/main/Bruteforce_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Unnecessary code dumps"
      ],
      "metadata": {
        "id": "WYGFJ_dNqQGY"
      }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jjfMZZin3Gho",
        "cellView": "form"
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      "outputs": [],
      "source": [
        "#@title\n",
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Upload kaggle api key\n",
        "# Import kaggle access key\n",
        "from google.colab import files\n",
        "\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "mdSyPmTEj2nj",
        "outputId": "640876e9-453e-4d98-be7f-b7898765ba5c",
        "cellView": "form",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5fd17152-bfa4-4c10-a444-474aae002a16\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5fd17152-bfa4-4c10-a444-474aae002a16\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
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              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"manuel14\",\"key\":\"3b9dfe18c100635bc3acf633996e3786\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Directory to hold kaggle api-key\n",
        "!mkdir ~/.kaggle\n",
        "\n",
        "!cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "2hNqT-E5j7S5",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "aYVzcT3Pj_FL",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Download Dataset from Kaggle\n",
        "!kaggle datasets download -d solarmainframe/ids-intrusion-csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAZ1ACEWkCJy",
        "outputId": "3064fa1d-c53f-4695-9009-80999e6ac927",
        "cellView": "form",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading ids-intrusion-csv.zip to /content\n",
            "100% 1.60G/1.60G [01:48<00:00, 16.6MB/s]\n",
            "100% 1.60G/1.60G [01:48<00:00, 15.9MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Create Dataset Folder\n",
        "!mkdir dataset"
      ],
      "metadata": {
        "id": "UamgCVYFlWOm",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Unzip dataset from kaggle into a folder\n",
        "!unzip ids-intrusion-csv.zip -d dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3wMWtBFalXY3",
        "outputId": "4db40583-504f-42b8-c518-b270e5c50265",
        "cellView": "form",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  ids-intrusion-csv.zip\n",
            "  inflating: dataset/02-14-2018.csv  \n",
            "  inflating: dataset/02-15-2018.csv  \n",
            "  inflating: dataset/02-16-2018.csv  \n",
            "  inflating: dataset/02-20-2018.csv  \n",
            "  inflating: dataset/02-21-2018.csv  \n",
            "  inflating: dataset/02-22-2018.csv  \n",
            "  inflating: dataset/02-23-2018.csv  \n",
            "  inflating: dataset/02-28-2018.csv  \n",
            "  inflating: dataset/03-01-2018.csv  \n",
            "  inflating: dataset/03-02-2018.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Save the downloaded dataset from kaggle to google drive\n",
        "# copy dataset to drive folder\n",
        "!cp ids-intrusion-csv.zip /content/drive/MyDrive/"
      ],
      "metadata": {
        "id": "bYU_RO6BlcsA",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "xg0GLIO6oNJN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Loading the dataset from my drive\n",
        "from google.colab import drive\n",
        "# drive.mount(\"/content/drive\")\n",
        "# Fetch zipfile from drive and extract it and store it in a folder called data\n",
        "!unzip /content/drive/MyDrive/ids-intrusion-csv.zip -d data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OvZQeFxIpo89",
        "outputId": "7dadf11e-2b19-483a-bbda-a46f0f2b3bde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/ids-intrusion-csv.zip\n",
            "  inflating: data/02-14-2018.csv     \n",
            "  inflating: data/02-15-2018.csv     \n",
            "  inflating: data/02-16-2018.csv     \n",
            "  inflating: data/02-20-2018.csv     \n",
            "  inflating: data/02-21-2018.csv     \n",
            "  inflating: data/02-22-2018.csv     \n",
            "  inflating: data/02-23-2018.csv     \n",
            "  inflating: data/02-28-2018.csv     \n",
            "  inflating: data/03-01-2018.csv     \n",
            "  inflating: data/03-02-2018.csv     \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns \n",
        "from IPython.display import Audio"
      ],
      "metadata": {
        "id": "QdbAxUcHoar2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Understanding the dataset.\n",
        "The CSE-CIC-IDS2018 dataset contains different classes of types of attack but for our research study we would be focusing on bruteforce attacks.\n",
        "\n",
        "For the purpose of our study we would be encoding the benign and bruteforce attacks according to the following format.\n",
        "\n",
        "- 0 - benign\n",
        "- 1 - Bruteforce attack"
      ],
      "metadata": {
        "id": "F3ncCztForGx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def encode_target(label):\n",
        "    if label==\"Benign\":\n",
        "        return 0 # Benign network traffic\n",
        "    if \"Bruteforce\" in label or \"BruteForce\" in label:\n",
        "        return 1 # Bruteforce attack"
      ],
      "metadata": {
        "id": "1L6-VlxVoaiL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "This function is to clean the dataset and drop missing data and rows with infinity and the target 'Label'.\n",
        "We are also dropping duplicate network traffic data. We have to drop the timestamp feature as it doesn't give us any relevant information that can help us in our prediction model.\n",
        "And also the destination port as we just want to predict the attack not the port being attacked.\n",
        "It also shows the percentage of Mising data and duplicate data\n",
        "\"\"\"\n",
        "def clean_data(data, time=False):\n",
        "    data = data.drop(\"Dst Port\", axis = 1)\n",
        "    \n",
        "    # Drop any rows with \"Infinity\" values or labels with the value \"Label\"\n",
        "    data = data.drop(data[data.eq(\"Infinity\").any(1)].index,axis=0)\n",
        "    data = data.drop(data[data.eq(\"Label\").any(1)].index,axis=0)\n",
        "    \n",
        "    # Percent of missing data \n",
        "    print(\"Missing Data:\",round(data.isnull().sum().sum()/len(data)*100,3),\"%\") # Just over 0.2%, very minimal so we can remove this \n",
        "    data = data.dropna()\n",
        "\n",
        "    # Percent of duplicated data\n",
        "    print(\"Duplicated Data:\", round(len(data[data.duplicated()])/len(data)*100,2),\"%\")\n",
        "    data = data.drop_duplicates()\n",
        "\n",
        "    if not time:\n",
        "        # Dropping the timestamp feature \n",
        "        data = data.drop(\"Timestamp\", axis = 1)\n",
        "\n",
        "    # One hot encoding the protocol feature \n",
        "    data['Protocol'] = data['Protocol'].astype(int)\n",
        "    dum_df = pd.get_dummies(data[\"Protocol\"],prefix=\"prot_\")\n",
        "    data = data.drop([\"Protocol\"],axis=1)\n",
        "    data = pd.concat([data,dum_df],axis=1) \n",
        "    \n",
        "    data[\"target\"] = np.vectorize(encode_target)(data[\"Label\"])\n",
        "    data = data.drop(\"Label\", axis = 1)\n",
        "\n",
        "    return data "
      ],
      "metadata": {
        "id": "XVYPG50ZoafK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bruteforce = pd.read_csv(\"/content/data/02-14-2018.csv\")"
      ],
      "metadata": {
        "id": "bqEWNBaJoaYj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the type of attacks present in the bruteforce dataset.\n",
        "bruteforce['Label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2_8ZzlIqzyd",
        "outputId": "51688cc7-f173-4312-a348-5af9e4808493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Benign            667626\n",
              "FTP-BruteForce    193360\n",
              "SSH-Bruteforce    187589\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cleaning our dataset\n",
        "bruteforce = clean_data(bruteforce,time=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mre78gnCqzvl",
        "outputId": "a9d99f3f-4b28-4285-9191-8ee07c4cd85a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Data: 0.217 %\n",
            "Duplicated Data: 21.65 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bruteforce.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "KcF48uNxqzsZ",
        "outputId": "bbc0d040-8fcc-4d27-947e-9e79e1fcdcea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Flow Duration  Tot Fwd Pkts  Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
              "0      112641719             3             0                0   \n",
              "1      112641466             3             0                0   \n",
              "2      112638623             3             0                0   \n",
              "3        6453966            15            10             1239   \n",
              "4        8804066            14            11             1143   \n",
              "\n",
              "   TotLen Bwd Pkts  Fwd Pkt Len Max  Fwd Pkt Len Min  Fwd Pkt Len Mean  \\\n",
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              "3             2273              744                0         82.600000   \n",
              "4             2209              744                0         81.642857   \n",
              "\n",
              "   Fwd Pkt Len Std  Bwd Pkt Len Max  ...  Active Max  Active Min   Idle Mean  \\\n",
              "0         0.000000                0  ...           0           0  56320859.5   \n",
              "1         0.000000                0  ...           0           0  56320733.0   \n",
              "2         0.000000                0  ...           0           0  56319311.5   \n",
              "3       196.741237              976  ...           0           0         0.0   \n",
              "4       203.745545              976  ...           0           0         0.0   \n",
              "\n",
              "     Idle Std  Idle Max  Idle Min  prot__0  prot__6  prot__17  target  \n",
              "0  139.300036  56320958  56320761        1        0         0       0  \n",
              "1  114.551299  56320814  56320652        1        0         0       0  \n",
              "2  301.934596  56319525  56319098        1        0         0       0  \n",
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          "metadata": {},
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          "data": {
            "text/plain": [
              "0    663094\n",
              "1    156668\n",
              "Name: target, dtype: int64"
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          "execution_count": 29
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              "Flow Duration      int64\n",
              "Tot Fwd Pkts       int64\n",
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              "TotLen Fwd Pkts    int64\n",
              "TotLen Bwd Pkts    int64\n",
              "                   ...  \n",
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              "Length: 80, dtype: object"
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              "       Flow Duration   Tot Fwd Pkts   Tot Bwd Pkts  TotLen Fwd Pkts  \\\n",
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              "      <td>1.713250e+05</td>\n",
              "      <td>308.192709</td>\n",
              "      <td>21.452856</td>\n",
              "      <td>55.677538</td>\n",
              "      <td>91.565220</td>\n",
              "      <td>522.013994</td>\n",
              "      <td>...</td>\n",
              "      <td>8.347933e+05</td>\n",
              "      <td>6.332049e+05</td>\n",
              "      <td>6.123993e+08</td>\n",
              "      <td>4.320386e+08</td>\n",
              "      <td>1.721485e+09</td>\n",
              "      <td>2.051077e+07</td>\n",
              "      <td>0.119512</td>\n",
              "      <td>0.442612</td>\n",
              "      <td>0.434688</td>\n",
              "      <td>0.393179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-9.190110e+11</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.160000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.915300e+04</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.600000e+01</td>\n",
              "      <td>1.320000e+02</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.751970e+06</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.128000e+03</td>\n",
              "      <td>1.581000e+03</td>\n",
              "      <td>572.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>80.333333</td>\n",
              "      <td>135.847392</td>\n",
              "      <td>976.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.200000e+08</td>\n",
              "      <td>5115.000000</td>\n",
              "      <td>9198.000000</td>\n",
              "      <td>8.591554e+06</td>\n",
              "      <td>1.339773e+07</td>\n",
              "      <td>64440.000000</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>11217.025225</td>\n",
              "      <td>15760.837590</td>\n",
              "      <td>1460.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.102401e+08</td>\n",
              "      <td>1.102401e+08</td>\n",
              "      <td>3.394503e+11</td>\n",
              "      <td>2.432682e+11</td>\n",
              "      <td>9.797810e+11</td>\n",
              "      <td>1.260300e+10</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 80 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f009d07e-74d1-4044-9645-b2a2c363903d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f009d07e-74d1-4044-9645-b2a2c363903d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datatypes = bruteforce.dtypes\n",
        "\n",
        "datatypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJk_1DlT-xBG",
        "outputId": "4371602c-6a77-4ae5-fc5e-4d0af716612b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Flow Duration      int64\n",
              "Tot Fwd Pkts       int64\n",
              "Tot Bwd Pkts       int64\n",
              "TotLen Fwd Pkts    int64\n",
              "TotLen Bwd Pkts    int64\n",
              "                   ...  \n",
              "Idle Min           int64\n",
              "prot__0            uint8\n",
              "prot__6            uint8\n",
              "prot__17           uint8\n",
              "target             int64\n",
              "Length: 80, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bruteforce_new = bruteforce[np.isfinite(bruteforce).all(1)]"
      ],
      "metadata": {
        "id": "Lp4ax3JGBxuI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0WKEDZoTBxnD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "12eeTrHX-w3z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RwPC3Ipm-RUU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ej663uwC-RRI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hQrp_V1C-RNr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "biWMSqTG-RHh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing Dataset after cleaning"
      ],
      "metadata": {
        "id": "wqfx18ICsNGH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate sum of benign records across partitions\n",
        "benign_records = bruteforce_new[\"target\"].value_counts()[0]\n",
        "\n",
        "# Get the number of attack records in each parition after cleaning \n",
        "brute_force_attacks = bruteforce_new[\"target\"].value_counts()[1]\n",
        "\n",
        "org_distribution = pd.DataFrame({\"Attack Type\":[\"Benign\",\"Brute-Force\"],\n",
        "                             \"Records\":[benign_records, brute_force_attacks]})\n",
        "\n",
        "org_distribution[\"Percentage\"] = round(org_distribution[\"Records\"]/org_distribution[\"Records\"].sum()*100,3)\n",
        "\n",
        "org_distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "AsMEw1j4qzl6",
        "outputId": "5cf94a94-1ce8-4256-90fd-c348becfc82e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Attack Type  Records  Percentage\n",
              "0       Benign   661847      80.859\n",
              "1  Brute-Force   156668      19.141"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40646a90-780c-47e8-9b1f-9e91fd83f1d8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Attack Type</th>\n",
              "      <th>Records</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Benign</td>\n",
              "      <td>661847</td>\n",
              "      <td>80.859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brute-Force</td>\n",
              "      <td>156668</td>\n",
              "      <td>19.141</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40646a90-780c-47e8-9b1f-9e91fd83f1d8')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      \n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-40646a90-780c-47e8-9b1f-9e91fd83f1d8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-40646a90-780c-47e8-9b1f-9e91fd83f1d8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Handling Data Imbalance\n",
        "\n",
        "From the above data we can see that the dataset is highly imbalanced and this can affect the results of our training.\n",
        "\n",
        "For the undersampling dataset we will balance classes by selecting records of each class equal to the number of classes in the class which appears the least. We will also choose a number of benign classes from each day equal to the proportion of total benign classes they represented in the original dataset.\n",
        "\n",
        "num_ben_in_one_class / total_number_of_benign_classes_in_original_data_set"
      ],
      "metadata": {
        "id": "ZDhLirWAsnhV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bruteforce_sample = bruteforce_new.loc[bruteforce[\"target\"]==1].sample(n=156668).copy()"
      ],
      "metadata": {
        "id": "cqWXcPQ6qzi0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bruteforce_benign = bruteforce_new.loc[bruteforce[\"target\"]==0].copy()"
      ],
      "metadata": {
        "id": "vKQWp6ZSoaVU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_benign = len(bruteforce_benign)"
      ],
      "metadata": {
        "id": "Vp_w-jYZoaSY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bruteforce_ratio = len(bruteforce_benign)/total_benign"
      ],
      "metadata": {
        "id": "01r2H2N-tw1f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bruteforce_benign = bruteforce_benign.sample(n = int(bruteforce_ratio*156668))"
      ],
      "metadata": {
        "id": "IqV5mPDEtwy5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To check if the combination has resulted in the correct number of benign class instances\n",
        "len(bruteforce_benign)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eeOid-7QtwwS",
        "outputId": "2ca8c582-8a76-4d86-e0f2-ff560929b4e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "156668"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "benign_sample = pd.concat([bruteforce_benign],axis=0)"
      ],
      "metadata": {
        "id": "vnx6UXQCtwtm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating our undersampled dataset\n",
        "\n",
        "This helps to balance the disparities in the dataset."
      ],
      "metadata": {
        "id": "gFkYGSwUuff6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "undersampled_dataset = pd.concat([benign_sample,bruteforce_sample],axis=0).sample(frac = 1)"
      ],
      "metadata": {
        "id": "kB4112HStwrf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_distribtuion = pd.DataFrame({'Attack Type':[\"Benign\",\"Brute-Force\"],\n",
        "                                 'Records': undersampled_dataset['target'].value_counts().sort_index().values})\n",
        "\n",
        "new_distribtuion[\"Percentage\"] = round(new_distribtuion[\"Records\"]/new_distribtuion[\"Records\"].sum()*100,3)\n",
        "new_distribtuion"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "1guLz0RAuvc-",
        "outputId": "f797067d-2e24-44f3-ee05-f73178e75d2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Attack Type  Records  Percentage\n",
              "0       Benign   156668        50.0\n",
              "1  Brute-Force   156668        50.0"
            ],
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              "    <div class=\"colab-df-container\">\n",
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              "      <th>Attack Type</th>\n",
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              "      <th>0</th>\n",
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              "      <td>50.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brute-Force</td>\n",
              "      <td>156668</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The new dataset now contains a balanced record of both benign and bruteforce attacks"
      ],
      "metadata": {
        "id": "JCGITFdYu4dB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(nrows=1,ncols=2, figsize=(15,6),dpi=500)\n",
        "\n",
        "sns.barplot(x=\"Attack Type\", y = 'Percentage', data = org_distribution, palette='Blues_d', ax=ax[0])\n",
        "sns.barplot(x=\"Attack Type\", y = 'Percentage', data = new_distribtuion, palette='Blues_d', ax=ax[1])\n",
        "\n",
        "ax[0].set_title(\"CSE-CIC-IDS2018 Class Distribution\")\n",
        "ax[1].set_title(\"CSE-CIC-IDS2018 Class Distribution with Undersampling\")\n",
        "ax[0].set_xlabel(\"\")\n",
        "\n",
        "ax[1].set_xlabel(\"\")\n",
        "ax[1].set_ylabel(\"\")\n",
        "plt.savefig('dist1.png')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 619
        },
        "id": "_P7dt8ZDuvZj",
        "outputId": "d96f0cf5-b2a2-44aa-c193-1f991328317a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 7500x3000 with 2 Axes>"
            ],
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the new undersampled dataset.\n",
        "undersampled_dataset.to_csv(\"/content/data/undersampled_cic.csv\", index=False)"
      ],
      "metadata": {
        "id": "98DKbFMPuvRP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the new dataset\n",
        "data = pd.read_csv(\"/content/data/undersampled_cic.csv\")\n",
        "cic_X = data.drop(\"target\",axis=1).values\n",
        "cic_y = data[\"target\"].values\n",
        "\n",
        "print(cic_X.shape[0], 'records',cic_X.shape[1],'features')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1sjm1NyKuvNZ",
        "outputId": "ceba7ea8-1f76-466f-fcbd-9dbe3fcb27df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313336 records 79 features\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CSl9HBJLoaH4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Building"
      ],
      "metadata": {
        "id": "U4vR84iRoU6L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Creating the Models we need\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn import metrics\n",
        "from sklearn import svm \n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential \n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, Conv2D, MaxPool2D, Flatten, SimpleRNN, LSTM, TimeDistributed\n",
        "from tensorflow.keras.callbacks import EarlyStopping \n",
        "from tensorflow.keras.utils import plot_model\n",
        "from keras.layers import CuDNNLSTM\n",
        "\n",
        "from timeit import timeit"
      ],
      "metadata": {
        "id": "njGvCIAclxWq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(42)\n",
        "np.random.seed(42)\n"
      ],
      "metadata": {
        "id": "QDpMyMoJmfY3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title SVM model\n",
        "class SVM:\n",
        "    def __init__(self, X, y, split, ae= False, ae_dim =0, ae_train_perc = 0.25):\n",
        "        self.split = split \n",
        "        self.ae = ae \n",
        "        self.ae_dim = ae_dim\n",
        "        self.ae_train_perc = ae_train_perc\n",
        "        \n",
        "        # For CSE-CIC-IDS2018 \n",
        "        if self.split:\n",
        "            self.X = X \n",
        "            self.y = y\n",
        "        else: \n",
        "            self.X_train, self.X_test = X\n",
        "            self.y_train, self.y_test = y \n",
        "            \n",
        "    def preprocess_data(self):\n",
        "        \n",
        "        if self.split:\n",
        "            # Split data into train and test split \n",
        "            X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, stratify = self.y)\n",
        "        else:\n",
        "            X_train, X_test, y_train, y_test = self.X_train, self.X_test, self.y_train, self.y_test\n",
        "        \n",
        "        min_max_scaler = MinMaxScaler()\n",
        "        X_train = min_max_scaler.fit_transform(X_train)\n",
        "        X_test = min_max_scaler.transform(X_test)\n",
        "        \n",
        "        if self.ae: \n",
        "            _ , X_val = train_test_split(X_train, test_size=self.ae_train_perc, random_state=42)\n",
        "            \n",
        "            encoder = Sequential()\n",
        "            encoder.add(Dense(self.ae_dim, activation='relu',input_shape=[X_val.shape[1]]))\n",
        "\n",
        "            decoder = Sequential()\n",
        "            decoder.add(Dense(X_val.shape[1], activation='relu', input_shape=[self.ae_dim], activity_regularizer=regularizers.l1(0.2))) # sparsity parameter\n",
        "\n",
        "            autoencoder = Sequential([encoder,decoder])\n",
        "            autoencoder.compile(loss='mse',optimizer = 'adam')\n",
        "\n",
        "            early_stop = EarlyStopping(monitor='loss', mode = 'auto', \n",
        "                                       patience = 5 , verbose =0, restore_best_weights=True)\n",
        "\n",
        "            autoencoder.fit(X_val, X_val, epochs=40, batch_size=256,verbose=0, callbacks=[early_stop])\n",
        "\n",
        "            X_train = encoder.predict(X_train)\n",
        "            X_test = encoder.predict(X_test)\n",
        "        \n",
        "        return X_train, X_test, y_train, y_test\n",
        "        \n",
        "    def evaluate(self, runs):\n",
        "        results = pd.DataFrame(columns=[\"acc\",\"prec\",\"rec\",\"f1\",\"far\"])\n",
        "        reports = []    \n",
        "        \n",
        "        for i in range(runs):\n",
        "            X_train, X_test, y_train, y_test = self.preprocess_data()\n",
        "\n",
        "            model = svm.SVC()\n",
        "            model.fit(X_train,y_train)\n",
        "\n",
        "            y_pred = model.predict(X_test)\n",
        "\n",
        "            acc = metrics.accuracy_score(y_test,y_pred)\n",
        "            prec = metrics.precision_score(y_test,y_pred,average='weighted')\n",
        "            rec = metrics.recall_score(y_test,y_pred,average='weighted')\n",
        "            f1 = metrics.f1_score(y_test,y_pred,average='weighted')\n",
        "            \n",
        "            confusion_matrix = metrics.confusion_matrix(y_test,y_pred)\n",
        "            FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix)  \n",
        "            FN = confusion_matrix.sum(axis=1) - np.diag(confusion_matrix)\n",
        "            TP = np.diag(confusion_matrix)\n",
        "            TN = confusion_matrix.sum() - (FP + FN + TP)\n",
        "            FAR = FP/(FP+TN)\n",
        "            \n",
        "            model_results = {'acc':acc, 'prec':prec, 'rec':rec, 'f1':f1,'far':FAR.mean()}\n",
        "            results = results.append(model_results, ignore_index= True)\n",
        "            print(f\"Model {i} Acc:\", acc)\n",
        "            \n",
        "            report = pd.DataFrame(metrics.classification_report(y_test,y_pred,output_dict=True)).transpose()\n",
        "            reports.append(report)\n",
        "            \n",
        "        self.acc = results.mean()[0], results.std()[0]\n",
        "        self.prec = results.mean()[1], results.std()[1]\n",
        "        self.rec = results.mean()[2], results.std()[2]\n",
        "        self.f1 = results.mean()[3], results.std()[3]\n",
        "        self.far = results.mean()[4], results.std()[4]\n",
        "        self.report = pd.concat(reports).groupby(level=0).mean()  "
      ],
      "metadata": {
        "id": "9o2nrXrOmfWQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Deep Neural Network\n",
        "class DNN:\n",
        "    def __init__(self, X, y, split, ae= False, ae_dim =0 , ae_train_perc = 0.25):\n",
        "        self.split = split \n",
        "        self.ae = ae \n",
        "        self.ae_dim = ae_dim\n",
        "        self.ae_train_perc = ae_train_perc\n",
        "        \n",
        "        # For CSE-CIC-IDS2018 \n",
        "        if self.split:\n",
        "            self.X = X \n",
        "            self.y = y\n",
        "        else: \n",
        "            self.X_train, self.X_test = X\n",
        "            self.y_train, self.y_test = y \n",
        "            \n",
        "        \n",
        "    def preprocess_data(self):\n",
        "        if self.split:\n",
        "            # Split data into train and test split \n",
        "            X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, stratify = self.y)\n",
        "        else:\n",
        "            X_train, X_test, y_train, y_test = self.X_train, self.X_test, self.y_train, self.y_test\n",
        "            \n",
        "        # Scale Data \n",
        "        min_max_scaler = MinMaxScaler()\n",
        "        X_train = min_max_scaler.fit_transform(X_train)\n",
        "        X_test = min_max_scaler.transform(X_test)\n",
        "        \n",
        "        # Encode Labels\n",
        "        y_train = pd.get_dummies(y_train).values\n",
        "        y_test = pd.get_dummies(y_test).values\n",
        "        \n",
        "        # Use Auto Encoder to perform dimensionality reduction \n",
        "        if self.ae: \n",
        "            _ , X_val = train_test_split(X_train, test_size=self.ae_train_perc, random_state=42)\n",
        "            \n",
        "            encoder = Sequential()\n",
        "            encoder.add(Dense(self.ae_dim, activation='relu',input_shape=[X_val.shape[1]]))\n",
        "\n",
        "            decoder = Sequential()\n",
        "            decoder.add(Dense(X_val.shape[1], activation='relu', input_shape=[self.ae_dim]))\n",
        "\n",
        "            autoencoder = Sequential([encoder,decoder])\n",
        "            autoencoder.compile(loss='mse',optimizer = 'adam')\n",
        "\n",
        "            early_stop = EarlyStopping(monitor='loss', mode = 'auto', \n",
        "                                       patience = 5 , verbose =0, restore_best_weights=True)\n",
        "\n",
        "            autoencoder.fit(X_val, X_val, epochs=40, batch_size=256,verbose=0, callbacks=[early_stop])\n",
        "\n",
        "            X_train = encoder.predict(X_train)\n",
        "            X_test = encoder.predict(X_test)\n",
        "        \n",
        "        return X_train, y_train, X_test, y_test\n",
        "        \n",
        "    def build_model(self, hidden_layers):\n",
        "        \n",
        "        # Split data into training and testing set, scale it and encode labels \n",
        "        X_train, y_train, X_test, y_test = self.preprocess_data()\n",
        "        \n",
        "        # Further divide Data into training and validation data\n",
        "        X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
        "        \n",
        "        model = Sequential()\n",
        "        \n",
        "        for layer in hidden_layers:\n",
        "            model.add(Dense(layer, activation='relu'))\n",
        "            model.add(BatchNormalization())\n",
        "            model.add(Dropout(0.2))\n",
        "\n",
        "        model.add(Dense(y_train.shape[1], activation = 'softmax'))\n",
        "        model.compile(loss='categorical_crossentropy', optimizer='adam')\n",
        "        \n",
        "        early_stop = EarlyStopping(monitor='val_loss', mode = 'auto', \n",
        "                                   patience = 5 , verbose =0, restore_best_weights=True)\n",
        "        \n",
        "        model.fit(x = X_train, y = y_train, epochs = 200, batch_size = 256, verbose = 0,\n",
        "                   validation_data = (X_val,y_val),callbacks = [early_stop])\n",
        "        \n",
        "        return model, X_test, y_test\n",
        "    \n",
        "    def evaluate_model(self, runs, hidden_layers, time=False):\n",
        "        results = pd.DataFrame(columns=[\"acc\",\"prec\",\"rec\",\"f1\",\"far\",\"Train Time\",\"Test Time\"])\n",
        "        reports = []\n",
        "        \n",
        "        # Train and evaluate model \n",
        "        for i in range(runs):\n",
        "            print(f\"Training Model {i} ...\")\n",
        "            \n",
        "            if time:\n",
        "                training_time = timeit(lambda: self.build_model(hidden_layers), number = 1)\n",
        "            else:\n",
        "                training_time= np.nan\n",
        "                \n",
        "            model, X_test, y_test = self.build_model(hidden_layers)\n",
        "            \n",
        "            print(f\"Evaluating Model {i} ...\")\n",
        "            if time:\n",
        "                testing_time = timeit(lambda: model.predict(X_test), number = 1)\n",
        "            else:\n",
        "                testing_time = np.nan\n",
        "      \n",
        "            y_pred = np.argmax(model.predict(X_test), axis=-1)\n",
        "            y_org = np.argmax(y_test,axis=1)\n",
        "            \n",
        "            acc = metrics.accuracy_score(y_org,y_pred)\n",
        "            prec = metrics.precision_score(y_org,y_pred,average='weighted')\n",
        "            rec = metrics.recall_score(y_org,y_pred,average='weighted')\n",
        "            f1 = metrics.f1_score(y_org,y_pred,average='weighted')\n",
        "            \n",
        "            confusion_matrix = metrics.confusion_matrix(y_org,y_pred)\n",
        "            FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix)  \n",
        "            FN = confusion_matrix.sum(axis=1) - np.diag(confusion_matrix)\n",
        "            TP = np.diag(confusion_matrix)\n",
        "            TN = confusion_matrix.sum() - (FP + FN + TP)\n",
        "            FAR = FP/(FP+TN)\n",
        "            \n",
        "            model_results = {'acc':acc, 'prec':prec, 'rec':rec, 'f1':f1,'far':FAR.mean(),\n",
        "                             'Train Time':training_time,'Test Time':testing_time}\n",
        "            results = results.append(model_results, ignore_index= True)\n",
        "            print(f\"Model {i} Acc:\", acc)\n",
        "            \n",
        "            # Creation of a classification report which is useful for examining per class metrics\n",
        "            report = pd.DataFrame(metrics.classification_report(y_org,y_pred,output_dict=True)).transpose()\n",
        "            reports.append(report)\n",
        "        \n",
        "        self.acc = results.mean()[0], results.std()[0]\n",
        "        self.prec = results.mean()[1], results.std()[1]\n",
        "        self.rec = results.mean()[2], results.std()[2]\n",
        "        self.f1 = results.mean()[3], results.std()[3]\n",
        "        self.far = results.mean()[4], results.std()[4]\n",
        "        self.train_time = results.mean()[5], results.std()[5]\n",
        "        self.test_time = results.mean()[6], results.std()[6]\n",
        "        self.report = pd.concat(reports).groupby(level=0).mean()"
      ],
      "metadata": {
        "id": "leqPGIFqmfTf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Convoluted Neural Network Model\n",
        "class CNN:\n",
        "    def __init__(self, X, y, split, image_dim, ae= False, ae_dim =0, ae_train_perc = 0.25):\n",
        "        self.split = split \n",
        "        self.ae = ae \n",
        "        self.ae_dim = ae_dim\n",
        "        self.image_dim = image_dim\n",
        "        self.ae_train_perc = ae_train_perc\n",
        "        \n",
        "        # For CSE-CIC-IDS2018 \n",
        "        if self.split:\n",
        "            # Add two extra features for 2D image inputs \n",
        "            a = np.zeros((len(X),1))\n",
        "            b = np.zeros((len(X),1))\n",
        "            self.X = np.hstack((X,a,b))\n",
        "            self.y = y\n",
        "        else: \n",
        "            self.X_train, self.X_test = X\n",
        "            self.y_train, self.y_test = y \n",
        "            \n",
        "    def preprocess_data(self):\n",
        "        \n",
        "        if self.split:\n",
        "            # Split data into train and test split \n",
        "            X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, stratify = self.y)\n",
        "        else:\n",
        "            X_train, X_test, y_train, y_test = self.X_train, self.X_test, self.y_train, self.y_test\n",
        "\n",
        "        # Scale Data \n",
        "        min_max_scaler = MinMaxScaler()\n",
        "        X_train = min_max_scaler.fit_transform(X_train)\n",
        "        X_test = min_max_scaler.transform(X_test)\n",
        "\n",
        "        # Encode Labels\n",
        "        y_train = pd.get_dummies(y_train).values\n",
        "        y_test = pd.get_dummies(y_test).values\n",
        "\n",
        "        # Use Auto Encoder to perform dimensionality reduction \n",
        "        if self.ae: \n",
        "            _ , X_val = train_test_split(X_train, test_size=self.ae_train_perc, random_state=42)\n",
        "            \n",
        "            encoder = Sequential()\n",
        "            encoder.add(Dense(self.ae_dim, activation='relu',input_shape=[X_val.shape[1]]))\n",
        "\n",
        "            decoder = Sequential()\n",
        "            decoder.add(Dense(X_val.shape[1], activation='relu', input_shape=[self.ae_dim]))\n",
        "\n",
        "            autoencoder = Sequential([encoder,decoder])\n",
        "            autoencoder.compile(loss='mse',optimizer = 'adam')\n",
        "\n",
        "            early_stop = EarlyStopping(monitor='loss', mode = 'auto', \n",
        "                                       patience = 5 , verbose =0, restore_best_weights=True)\n",
        "\n",
        "            autoencoder.fit(X_val, X_val, epochs=40, batch_size=256,verbose=0, callbacks=[early_stop])\n",
        "\n",
        "            X_train = encoder.predict(X_train)\n",
        "            X_test = encoder.predict(X_test)\n",
        "\n",
        "        # Reshape the data into 2D input arrays\n",
        "        X_train = X_train.reshape(len(X_train), self.image_dim, self.image_dim,1)\n",
        "        X_test = X_test.reshape(len(X_test), self.image_dim, self.image_dim,1) \n",
        "\n",
        "        return X_train, y_train, X_test, y_test\n",
        "        \n",
        "    def display_class_image(self, classes):\n",
        "        X_train, y_train, X_test, y_test = self.preprocess_data()\n",
        "        y_train_ser = pd.Series(np.argmax(y_train,axis=1))\n",
        "        fig, axs = plt.subplots(nrows =1 , ncols = (len(classes)), figsize= (15,6))\n",
        "        for c in classes.keys():\n",
        "            X_image = X_train[y_train_ser[y_train_ser==c].index[0]]\n",
        "            axs[c].imshow(X_image[:,:,0], cmap='gray')\n",
        "            axs[c].set_title(classes[c])\n",
        "            axs[c].axis('off')\n",
        "        plt.show()\n",
        "        \n",
        "    def build_model(self):\n",
        "        \n",
        "        # Split data into training and testing set, scale it and encode labels \n",
        "        X_train, y_train, X_test, y_test = self.preprocess_data()\n",
        "    \n",
        "        # Further divide Data into training and validation data\n",
        "        X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, stratify= y_train)\n",
        "\n",
        "        model = Sequential()\n",
        "\n",
        "        model.add(Conv2D(filters=32, kernel_size=(2,2), input_shape=(self.image_dim,self.image_dim,1), padding = 'same', activation='relu'))\n",
        "        model.add(BatchNormalization())\n",
        "        \n",
        "        model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "\n",
        "        \n",
        "        model.add(Conv2D(filters=16, kernel_size=(2,2), padding = 'same', activation='relu'))\n",
        "        model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "        \n",
        "        model.add(Flatten())\n",
        "        \n",
        "        model.add(Dense(64,activation='relu'))\n",
        "        model.add(Dropout(0.2))\n",
        "        \n",
        "        model.add(Dense(y_train.shape[1],activation = \"softmax\"))\n",
        "\n",
        "        model.compile(loss=\"categorical_crossentropy\", optimizer = 'adam')\n",
        "        \n",
        "        early_stop = EarlyStopping(monitor='val_loss', mode = 'auto', \n",
        "                                   patience = 5 , verbose = 0, restore_best_weights=True)\n",
        "        \n",
        "        model.fit(x = X_train, y = y_train, epochs = 100, batch_size = 256, verbose = 0, \n",
        "                  callbacks = [early_stop], validation_data = (X_val,y_val))\n",
        "        \n",
        "        return model, X_test, y_test\n",
        "        \n",
        " \n",
        "    def evaluate_model(self, runs, time=False):\n",
        "        results = pd.DataFrame(columns=[\"acc\",\"prec\",\"rec\",\"f1\",\"far\",\"Train Time\",\"Test Time\"])\n",
        "        reports = []\n",
        "        \n",
        "        # Train and evaluate model \n",
        "        for i in range(runs):\n",
        "            \n",
        "            print(f\"Training Model {i} ...\")\n",
        "            \n",
        "            if time:\n",
        "                training_time = timeit(lambda: self.build_model(), number = 1)\n",
        "            else:   \n",
        "                training_time= np.nan \n",
        "            model, X_test, y_test = self.build_model()\n",
        "            \n",
        "            print(f\"Evaluating Model {i} ...\")\n",
        "            \n",
        "            if time:\n",
        "                  testing_time = timeit(lambda: model.predict(X_test), number = 1)\n",
        "            else:        \n",
        "                testing_time = np.nan\n",
        "                \n",
        "            y_pred = np.argmax(model.predict(X_test), axis=-1)\n",
        "            y_org = np.argmax(y_test,axis=1)\n",
        "            \n",
        "            acc = metrics.accuracy_score(y_org,y_pred)\n",
        "            prec = metrics.precision_score(y_org,y_pred,average='weighted')\n",
        "            rec = metrics.recall_score(y_org,y_pred,average='weighted')\n",
        "            f1 = metrics.f1_score(y_org,y_pred,average='weighted')\n",
        "            \n",
        "            confusion_matrix = metrics.confusion_matrix(y_org,y_pred)\n",
        "            FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix)  \n",
        "            FN = confusion_matrix.sum(axis=1) - np.diag(confusion_matrix)\n",
        "            TP = np.diag(confusion_matrix)\n",
        "            TN = confusion_matrix.sum() - (FP + FN + TP)\n",
        "            FAR = FP/(FP+TN)\n",
        "            \n",
        "            model_results = {'acc':acc, 'prec':prec, 'rec':rec, 'f1':f1,'far':FAR.mean(),\n",
        "                             'Train Time':training_time,'Test Time':testing_time}\n",
        "            results = results.append(model_results, ignore_index= True)\n",
        "            print(f\"Model {i} Acc:\", acc)\n",
        "            \n",
        "            # Creation of a classification report which is useful for examining per class metrics\n",
        "            report = pd.DataFrame(metrics.classification_report(y_org,y_pred,output_dict=True)).transpose()\n",
        "            reports.append(report)\n",
        "        \n",
        "        self.acc = results.mean()[0], results.std()[0]\n",
        "        self.prec = results.mean()[1], results.std()[1]\n",
        "        self.rec = results.mean()[2], results.std()[2]\n",
        "        self.f1 = results.mean()[3], results.std()[3]\n",
        "        self.far = results.mean()[4], results.std()[4]\n",
        "        self.train_time = results.mean()[5], results.std()[5]\n",
        "        self.test_time = results.mean()[6], results.std()[6]\n",
        "        self.report = pd.concat(reports).groupby(level=0).mean()"
      ],
      "metadata": {
        "id": "6li6j61LmfQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title LSTM model\n",
        "class RNN_LSTM:\n",
        "    def __init__(self, X, y, split, num_of_classes, ae= False, ae_dim =0, ae_train_perc = 0.25):\n",
        "        self.split = split \n",
        "        self.ae = ae \n",
        "        self.ae_dim = ae_dim\n",
        "        self.num_of_classes = num_of_classes\n",
        "        self.ae_train_perc = ae_train_perc\n",
        "        \n",
        "        # For CSE-CIC-IDS2018 \n",
        "        if self.split:\n",
        "            # Add two extra features for 2D image inputs \n",
        "            a = np.zeros((len(X),1))\n",
        "            b = np.zeros((len(X),1))\n",
        "            self.X = np.hstack((X,a,b))\n",
        "            self.y = y\n",
        "        else: \n",
        "            self.X_train, self.X_test = X\n",
        "            self.y_train, self.y_test = y \n",
        "            \n",
        "    def preprocess_data(self):\n",
        "        \n",
        "        if self.split:\n",
        "            # Split data into train and test split \n",
        "            X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.3, stratify = self.y)\n",
        "        else:\n",
        "            X_train, X_test, y_train, y_test = self.X_train, self.X_test, self.y_train, self.y_test\n",
        "\n",
        "        # Scale Data \n",
        "        min_max_scaler = MinMaxScaler()\n",
        "        X_train = min_max_scaler.fit_transform(X_train)\n",
        "        X_test = min_max_scaler.transform(X_test)\n",
        "\n",
        "        # Use Auto Encoder to perform dimensionality reduction \n",
        "        if self.ae: \n",
        "            _ , X_val = train_test_split(X_train, test_size=self.ae_train_perc, random_state=42)\n",
        "            \n",
        "            encoder = Sequential()\n",
        "            encoder.add(Dense(self.ae_dim, activation='relu',input_shape=[X_val.shape[1]]))\n",
        "\n",
        "            decoder = Sequential()\n",
        "            decoder.add(Dense(X_val.shape[1], activation='relu', input_shape=[self.ae_dim]))\n",
        "\n",
        "            autoencoder = Sequential([encoder,decoder])\n",
        "            autoencoder.compile(loss='mse',optimizer = 'adam')\n",
        "\n",
        "            early_stop = EarlyStopping(monitor='loss', mode = 'auto', \n",
        "                                       patience = 5 , verbose =0, restore_best_weights=True)\n",
        "\n",
        "            autoencoder.fit(X_val, X_val, epochs=40, batch_size=256,verbose=0, callbacks=[early_stop])\n",
        "\n",
        "            X_train = encoder.predict(X_train)\n",
        "            X_test = encoder.predict(X_test)\n",
        "\n",
        "        # Reshape the data into 3D input arrays which include a time dimension as input for LSTM\n",
        "        X_train = X_train.reshape(len(X_train), 1, X_train.shape[1])\n",
        "        X_test = X_test.reshape(len(X_test), 1, X_test.shape[1]) \n",
        "\n",
        "        return X_train, y_train, X_test, y_test\n",
        "      \n",
        "        \n",
        "    def build_model(self):\n",
        "        # Split data into training and testing set, scale it and encode labels \n",
        "        X_train, y_train, X_test, y_test = self.preprocess_data()\n",
        "        \n",
        "        # Further divide Data into training and validation data\n",
        "        X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42, stratify= y_train)\n",
        "        \n",
        "        input_dim = X_train.shape[1]\n",
        "        model = Sequential()\n",
        "\n",
        "        model.add(LSTM(64, input_shape = (1,X_train.shape[2]), return_sequences=True, unroll=True))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "        model.add(LSTM(32,unroll=True))\n",
        "        model.add(BatchNormalization())\n",
        "        model.add(Dropout(0.2))\n",
        "\n",
        "\n",
        "        model.add(Dense(self.num_of_classes, activation ='softmax'))\n",
        "        model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')\n",
        "\n",
        "        early_stop = EarlyStopping(monitor='val_loss', mode = 'auto', \n",
        "                           patience = 5 , verbose =1, restore_best_weights=True)\n",
        "\n",
        "        model.fit(x = X_train, y = y_train, epochs = 100, batch_size = 256, verbose = 0,\n",
        "                  callbacks = [early_stop], validation_data = (X_val,y_val))\n",
        "        \n",
        "        return model, X_test, y_test\n",
        "    \n",
        "    def evaluate_model(self, runs,time=False):\n",
        "        results = pd.DataFrame(columns=[\"acc\",\"prec\",\"rec\",\"f1\",\"far\",\"Train Time\",\"Test Time\"])\n",
        "        reports = []\n",
        "        \n",
        "        self.preprocess_data()\n",
        "        \n",
        "        for i in range(runs):\n",
        "            print(f\"Training Model {i} ...\")\n",
        "            if time:\n",
        "                training_time = timeit(lambda: self.build_model(), number = 1)\n",
        "            else:\n",
        "                training_time= np.nan\n",
        "                \n",
        "            model, X_test, y_test = self.build_model()\n",
        "            print(f\"Evaluating Model {i} ...\")\n",
        "            \n",
        "            if time:\n",
        "                testing_time = timeit(lambda: model.predict(X_test), number = 1)\n",
        "            else:\n",
        "                testing_time=1\n",
        "                \n",
        "            y_pred = np.argmax(model.predict(X_test), axis=-1)\n",
        "            \n",
        "            acc = metrics.accuracy_score(y_test,y_pred)\n",
        "            prec = metrics.precision_score(y_test,y_pred,average='weighted')\n",
        "            rec = metrics.recall_score(y_test,y_pred,average='weighted')\n",
        "            f1 = metrics.f1_score(y_test,y_pred,average='weighted')\n",
        "            \n",
        "            confusion_matrix = metrics.confusion_matrix(y_test,y_pred)\n",
        "            FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix)  \n",
        "            FN = confusion_matrix.sum(axis=1) - np.diag(confusion_matrix)\n",
        "            TP = np.diag(confusion_matrix)\n",
        "            TN = confusion_matrix.sum() - (FP + FN + TP)\n",
        "            FAR = FP/(FP+TN)\n",
        "            \n",
        "            model_results = {'acc':acc, 'prec':prec, 'rec':rec, 'f1':f1,'far':FAR.mean(),\n",
        "                             'Train Time':training_time,'Test Time':testing_time}\n",
        "            results = results.append(model_results, ignore_index= True)\n",
        "            print(f\"Model {i} Acc:\", acc)\n",
        "            \n",
        "            # Creation of a classification report which is useful for examining per class metrics\n",
        "            report = pd.DataFrame(metrics.classification_report(y_test,y_pred,output_dict=True)).transpose()\n",
        "            reports.append(report)\n",
        "        \n",
        "        self.acc = results.mean()[0], results.std()[0]\n",
        "        self.prec = results.mean()[1], results.std()[1]\n",
        "        self.rec = results.mean()[2], results.std()[2]\n",
        "        self.f1 = results.mean()[3], results.std()[3]\n",
        "        self.far = results.mean()[4], results.std()[4]\n",
        "        self.train_time = results.mean()[5], results.std()[5]\n",
        "        self.test_time = results.mean()[6], results.std()[6]\n",
        "        self.report = pd.concat(reports).groupby(level=0).mean()"
      ],
      "metadata": {
        "id": "Hr4HrQkRmfN_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics "
      ],
      "metadata": {
        "id": "K8_fGbkomfLD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating our baseline evaluation. \n",
        "\n",
        "Based on previous evaluation using standard machine learning results as a comparison for our own results.\n",
        "\n",
        "We would be using the results from SVM(Support Vector Machines) models"
      ],
      "metadata": {
        "id": "LO65BSWTw2xB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cic_svm = SVM(cic_X,cic_y,split=True)\n",
        "cic_svm.evaluate(10)"
      ],
      "metadata": {
        "id": "sHHqWscgme7m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d2549bc-b02a-4760-b07d-e10477ab8261"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model 0 Acc: 0.9991276688545867\n",
            "Model 1 Acc: 0.9989680960840842\n",
            "Model 2 Acc: 0.9993829852873906\n",
            "Model 3 Acc: 0.9991276688545867\n",
            "Model 4 Acc: 0.9990957543004861\n",
            "Model 5 Acc: 0.9990744779310858\n",
            "Model 6 Acc: 0.9990638397463857\n",
            "Model 7 Acc: 0.9991170306698864\n",
            "Model 8 Acc: 0.9990106488228848\n",
            "Model 9 Acc: 0.99934043254859\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Deep Neural Network results\n",
        "\n",
        "cic_dnn = DNN(X = cic_X, y = cic_y, split = True)\n",
        "cic_dnn.evaluate_model(5,[64,32,16], time=True)"
      ],
      "metadata": {
        "id": "ezWf8HRmme5B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dcc0cef-03fc-4ee1-e20c-ea5e5115bec0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Model 0 ...\n",
            "Evaluating Model 0 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 0 Acc: 0.9999787236305997\n",
            "Training Model 1 ...\n",
            "Evaluating Model 1 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 1 Acc: 0.9998404272294975\n",
            "Training Model 2 ...\n",
            "Evaluating Model 2 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 2 Acc: 0.9999574472611994\n",
            "Training Model 3 ...\n",
            "Evaluating Model 3 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 3 Acc: 0.9999255327070988\n",
            "Training Model 4 ...\n",
            "Evaluating Model 4 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 4 Acc: 0.9999680854458995\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convolutional Neural Network results\n",
        "cic_cnn = CNN(X = cic_X, y = cic_y, split=True, image_dim=9) # 81 Features = 9x9 Images\n",
        "cic_cnn.display_class_image({0:'Benign', 1:'Bruteforce Attack'})"
      ],
      "metadata": {
        "id": "cQzou8krme2K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "bc86a2c2-c4dc-4308-e34f-c8da62e0159f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cic_cnn.evaluate_model(5,time=True)"
      ],
      "metadata": {
        "id": "fBsx5veUmezx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9022d46-782f-4363-f867-85b59bd2c1f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Model 0 ...\n",
            "Evaluating Model 0 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 0 Acc: 0.9999680854458995\n",
            "Training Model 1 ...\n",
            "Evaluating Model 1 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 1 Acc: 0.9999787236305997\n",
            "Training Model 2 ...\n",
            "Evaluating Model 2 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 2 Acc: 0.9999680854458995\n",
            "Training Model 3 ...\n",
            "Evaluating Model 3 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 3 Acc: 0.9999468090764991\n",
            "Training Model 4 ...\n",
            "Evaluating Model 4 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 4 Acc: 0.9999148945223987\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM results\n",
        "cic_lstm = RNN_LSTM(X=cic_X, y = cic_y, split = True, num_of_classes=7)"
      ],
      "metadata": {
        "id": "TB1LwpPcmexi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cic_lstm.evaluate_model(5, time=True)"
      ],
      "metadata": {
        "id": "onIZto1SmevA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdcb0b4f-b40f-431d-9a7b-01ddc84acc5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Model 0 ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 11.\n",
            "Epoch 16: early stopping\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 12.\n",
            "Epoch 17: early stopping\n",
            "Evaluating Model 0 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 0 Acc: 0.9999680854458995\n",
            "Training Model 1 ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_4 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_5 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 8.\n",
            "Epoch 13: early stopping\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_6 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_7 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 6.\n",
            "Epoch 11: early stopping\n",
            "Evaluating Model 1 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 6s 2ms/step\n",
            "Model 1 Acc: 0.9998723417835981\n",
            "Training Model 2 ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_8 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_9 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 25.\n",
            "Epoch 30: early stopping\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_10 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_11 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 7.\n",
            "Epoch 12: early stopping\n",
            "Evaluating Model 2 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 6s 2ms/step\n",
            "Model 2 Acc: 0.9999042563376985\n",
            "Training Model 3 ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_12 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_13 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 10.\n",
            "Epoch 15: early stopping\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_14 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_15 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 19.\n",
            "Epoch 24: early stopping\n",
            "Evaluating Model 3 ...\n",
            "2938/2938 [==============================] - 6s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 3 Acc: 0.9999468090764991\n",
            "Training Model 4 ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_16 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_17 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 10.\n",
            "Epoch 15: early stopping\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_18 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "WARNING:tensorflow:Layer lstm_19 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Restoring model weights from the end of the best epoch: 16.\n",
            "Epoch 21: early stopping\n",
            "Evaluating Model 4 ...\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "2938/2938 [==============================] - 5s 2ms/step\n",
            "Model 4 Acc: 0.9999680854458995\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Results Comparison\n"
      ],
      "metadata": {
        "id": "-ukM7WtOy27F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def autolabel(rects, xpos='center', perc=True):\n",
        "    ha = {'center': 'center', 'right': 'left', 'left': 'right'}\n",
        "    offset = {'center': 0, 'right': 1, 'left': -1}\n",
        "\n",
        "    for rect in rects:\n",
        "        height = rect.get_height()\n",
        "        if perc:\n",
        "            ax.annotate('{}%'.format(height),\n",
        "                        xy=(rect.get_x() + rect.get_width() / 2, height),\n",
        "                        xytext=(offset[xpos]*3, 1),  # use 3 points offset\n",
        "                        textcoords=\"offset points\",  # in both directions\n",
        "                        ha=ha[xpos], va='bottom',\n",
        "                        fontsize = 11)\n",
        "        else:\n",
        "            ax.annotate('{}'.format(height),\n",
        "                        xy=(rect.get_x() + rect.get_width() / 2, height),\n",
        "                        xytext=(offset[xpos]*3, 1),  # use 3 points offset\n",
        "                        textcoords=\"offset points\",  # in both directions\n",
        "                        ha=ha[xpos], va='bottom',\n",
        "                        fontsize = 11)"
      ],
      "metadata": {
        "id": "eFICDzWdmesU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cic_acc_means, cic_acc_std = (round(cic_svm.acc[0]*100,3),round(cic_dnn.acc[0]*100,3), round(cic_cnn.acc[0]*100,3), round(cic_lstm.acc[0]*100,3)), \\\n",
        "                     (cic_svm.acc[1]*100,cic_dnn.acc[1]*100, cic_cnn.acc[1]*100, cic_lstm.acc[1]*100)\n",
        "    \n",
        "cic_f1_means, cic_f1_std = (round(cic_svm.f1[0]*100,3), round(cic_dnn.f1[0]*100,3), round(cic_cnn.f1[0]*100,3), round(cic_lstm.f1[0]*100,3)), \\\n",
        "                         (cic_svm.f1[1]*100, cic_dnn.f1[1]*100, cic_cnn.f1[1]*100, cic_lstm.f1[1]*100)\n",
        "    \n",
        "ind = np.arange(len(cic_acc_means))  # the x locations for the groups\n",
        "width = 0.35  # the width of the bars\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,6))\n",
        "rects1 = ax.bar(ind - width/2, cic_acc_means, width, yerr=cic_acc_std,\n",
        "                label='Accuracy', color='grey')\n",
        "rects2 = ax.bar(ind + width/2, cic_f1_means, width, yerr=cic_f1_std,\n",
        "                label='F1 Score')\n",
        "\n",
        "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_title('Model Performance on Undersampled CSE-CIC-IDS2018')\n",
        "ax.set_xticks(ind)\n",
        "ax.set_xticklabels(('SVM','DNN', 'CNN', 'LSTM'))\n",
        "ax.legend()\n",
        "\n",
        "autolabel(rects1, \"left\")\n",
        "autolabel(rects2, \"right\")\n",
        "\n",
        "fig.tight_layout()\n",
        "plt.ylim(70,90)\n",
        "plt.legend(bbox_to_anchor=(1,1))\n",
        "plt.savefig(\"acc_f1_cic.pdf\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "vFB4_7ZdmenO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "e7170f1c-cb84-4bd6-f57a-466c1756c755"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cic_far_means, cic_far_std = (round(cic_svm.far[0]*100,3),round(cic_dnn.far[0]*100,3), round(cic_cnn.far[0]*100,3), round(cic_lstm.far[0]*100,3)), \\\n",
        "                     (cic_svm.far[1]*100, cic_dnn.far[1]*100, cic_cnn.far[1]*100, cic_lstm.far[1]*100)\n",
        "\n",
        "    \n",
        "ind = np.arange(len(cic_far_means))  # the x locations for the groups\n",
        "width = 0.35  # the width of the bars\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12,6))\n",
        "rects1 = ax.bar(ind, cic_far_means, width, yerr=cic_far_std,\n",
        "                label='False Alarm Rate', color='gold')\n",
        "\n",
        "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
        "ax.set_ylabel('False Alarm Rate')\n",
        "ax.set_title('Model Performance on Undersampled CSE-CIC-IDS2018')\n",
        "ax.set_xticks(ind)\n",
        "ax.set_xticklabels(('SVM','DNN', 'CNN', 'LSTM'))\n",
        "ax.legend()\n",
        "\n",
        "autolabel(rects1, \"left\")\n",
        "fig.tight_layout()\n",
        "\n",
        "#plt.ylim(70,90)\n",
        "plt.legend(bbox_to_anchor=(1,1))\n",
        "plt.savefig(\"far_cic.pdf\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kfFwEWB_medt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "f3a7d818-f862-4e07-fcde-338bac131b91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_time_means, train_time_std = (round(cic_dnn.train_time[0],5), round(cic_cnn.train_time[0],5), round(cic_lstm.train_time[0],5)), \\\n",
        "                     (cic_dnn.train_time[1], cic_cnn.train_time[1], cic_lstm.train_time[1])\n",
        "\n",
        "    \n",
        "ind = np.arange(len(train_time_means))  # the x locations for the groups\n",
        "width = 0.35  # the width of the bars\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8,6))\n",
        "rects1 = ax.bar(ind, train_time_means, width, yerr=train_time_std,\n",
        "                label='Training Time', color='lightblue')\n",
        "\n",
        "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
        "ax.set_ylabel('Training Time (seconds)')\n",
        "ax.set_title('Model Performance on Undersampled CSE-CIC-IDS2018')\n",
        "ax.set_xticks(ind)\n",
        "ax.set_xticklabels(('DNN', 'CNN', 'LSTM'))\n",
        "ax.legend()\n",
        "\n",
        "autolabel(rects1, \"left\",perc=False)\n",
        "fig.tight_layout()\n",
        "\n",
        "#plt.ylim(70,90)\n",
        "plt.legend(loc=2)\n",
        "#plt.xlabel(\"Model\")\n",
        "plt.savefig(\"train_time_cic.pdf\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "iY9RnbgDzKce",
        "outputId": "0f9edcc7-fc12-4186-e1dc-2d5d00d04e30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_time_means, test_time_std = (round(cic_dnn.test_time[0],5), round(cic_cnn.test_time[0],5), round(cic_lstm.test_time[0],5)), \\\n",
        "                     (cic_dnn.test_time[1], cic_cnn.test_time[1], cic_lstm.test_time[1])\n",
        "\n",
        "    \n",
        "ind = np.arange(len(test_time_means))  # the x locations for the groups\n",
        "width = 0.35  # the width of the bars\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8,6))\n",
        "rects1 = ax.bar(ind, test_time_means, width, yerr=test_time_std,\n",
        "                label='Inference Time', color='maroon')\n",
        "\n",
        "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
        "ax.set_ylabel('Inference Time (seconds)')\n",
        "ax.set_title('Model Performance on Undersampled CSE-CIC-IDS2018')\n",
        "ax.set_xticks(ind)\n",
        "ax.set_xticklabels(('DNN', 'CNN', 'LSTM'))\n",
        "ax.legend()\n",
        "\n",
        "autolabel(rects1, \"left\",perc=False)\n",
        "fig.tight_layout()\n",
        "\n",
        "#plt.ylim(70,90)\n",
        "plt.legend(loc=2)\n",
        "#plt.xlabel(\"Model\")\n",
        "plt.savefig(\"test_time_cic.pdf\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "Elsc4H6vzQx-",
        "outputId": "dc77996d-1a84-4333-e946-a45d16a94bae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZzobaTjczQXh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iYgYahiAzQUp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W2p0W_FBzQR1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}