Final_static_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "#### import library \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "fM9UDTEVjZnn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install category-encoders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNOdHK2HUTI0",
        "outputId": "18c237f1-a812-4db4-e0ad-95cd2792e905"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: category-encoders in /usr/local/lib/python3.7/dist-packages (2.5.1.post0)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from category-encoders) (1.21.6)\n",
            "Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from category-encoders) (1.3.5)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from category-encoders) (0.12.2)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from category-encoders) (1.0.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from category-encoders) (1.7.3)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.7/dist-packages (from category-encoders) (0.5.3)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.5->category-encoders) (2022.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.5->category-encoders) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5.1->category-encoders) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category-encoders) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category-encoders) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "BkAZlRf_wJ7M"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import  accuracy_score , plot_confusion_matrix ,f1_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from collections import Counter\n",
        "import pickle\n",
        "import seaborn as sns\n",
        "import xgboost as xgb\n",
        "from xgboost import XGBClassifier\n",
        "import xgboost\n",
        "xgboost.__version__\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.feature_selection import f_classif\n",
        "from sklearn.feature_selection import mutual_info_classif\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.metrics import classification_report, ConfusionMatrixDisplay, accuracy_score, confusion_matrix\n",
        "from sklearn.feature_selection import f_classif\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import recall_score\n",
        "import joblib\n",
        "from category_encoders.hashing import HashingEncoder\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Helper Function"
      ],
      "metadata": {
        "id": "3lFdwyFghz0i"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "3VTUGSE1cweL"
      },
      "outputs": [],
      "source": [
        "# function train model for return predictopn of baseline model i used it with train test split only \n",
        "def baseline_model(model,X_train,y_train,X_test):\n",
        "    model.fit(X_train, y_train)\n",
        "    model_pred = model.predict(X_test)\n",
        "    return model_pred\n",
        "\n",
        "\n",
        "#Print the confusion matrix and classification report \n",
        "def model_information(model_pred,y_test,title):\n",
        " \n",
        "    print(\"F1 Score Accuracy\\t\",f1_score(y_test, model_pred))\n",
        "    print(\"\\n\")\n",
        "    print(classification_report(y_test, model_pred))    \n",
        "    cm = confusion_matrix(y_test, model_pred, labels=[0,1])\n",
        "    print(cm)\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=[0,1])\n",
        "    disp.plot()\n",
        "    plt.title(title)\n",
        "    plt.show()\n",
        "    pass\n",
        "\n",
        "\n",
        "\n",
        "## Plot KDE and Hist of Each column\n",
        "\n",
        "def kde_hist_plot(df,row,col):\n",
        "  fig, ax = plt.subplots(row,col, figsize=(20, 50)) \n",
        "  fig.subplots_adjust(hspace = .2, wspace=.2, )\n",
        "  ax = ax.ravel()\n",
        "  for i in range(len(df.columns)):\n",
        "      ax[i] = df.iloc[:,i].plot(kind='hist', ax=ax[i])\n",
        "      ax[i] = df.iloc[:,i].plot(kind='kde', ax=ax[i], secondary_y=True)\n",
        "      plt.title(df.columns[i])\n",
        "  pass\n",
        "\n",
        "\n",
        "### feature selection this function support two type of feature selection <annova, mutual information>\n",
        "### if i assign the parameter annova with true that will go to annova if False that will apply mutual information\n",
        "def feature_selection(X_train, Y_train, X_test, Y_test,model, n_features, anova):\n",
        "  f1_acc = []\n",
        "  feature = []\n",
        "  for i in range(1, n_features):\n",
        "    if anova:\n",
        "      selecter = SelectKBest(f_classif, k = i)\n",
        "    else:\n",
        "      selecter = SelectKBest(mutual_info_classif, k=i)\n",
        "    X_new = selecter.fit_transform(X_train, Y_train)\n",
        "    X_test_new = selecter.transform(X_test)\n",
        "    model.fit(X_new, Y_train)\n",
        "    y_pred = model.predict(X_test_new)\n",
        "    f1_acc.append(f1_score(Y_test, y_pred))\n",
        "    feature.append(i)\n",
        "    print(\"feature \" + str(i) + \" finished\")\n",
        "  return f1_acc,feature\n",
        "  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Read that static dataset "
      ],
      "metadata": {
        "id": "cU1Po--WetHy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3kiKJuXog2NS",
        "outputId": "9b77aa39-3d3a-4904-be80-5a0334968a8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(268074, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "url = \"/content/Static_dataset.csv\"\n",
        "data = pd.read_csv(url)\n",
        "data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### EDA Before preprocessing"
      ],
      "metadata": {
        "id": "rpR6eKq0vXcf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "id": "Kk3IvEPpfPuy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "8b6f3dc9-3338-4ee9-ff07-6a69b5f0966c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  timestamp  FQDN_count  subdomain_length  upper  lower  numeric   entropy  \\\n",
              "0   56:19.8          27                10      0     10       11  2.570417   \n",
              "1   07:23.9          27                10      0     10       11  2.767195   \n",
              "2   23:15.1          26                 9      0     10       10  2.742338   \n",
              "3   04:51.9          27                10      0     10       11  2.570417   \n",
              "4   12:44.0          15                 9      0     11        0  2.929439   \n",
              "\n",
              "   special  labels  labels_max  labels_average longest_word    sld  len  \\\n",
              "0        6       6           7        3.666667            2    192   14   \n",
              "1        6       6           7        3.666667            2    192   14   \n",
              "2        6       6           7        3.500000            2    192   13   \n",
              "3        6       6           7        3.666667            2    192   14   \n",
              "4        4       3           5        4.333333        local  local   15   \n",
              "\n",
              "   subdomain  Target Attack  \n",
              "0          1              1  \n",
              "1          1              1  \n",
              "2          1              0  \n",
              "3          1              1  \n",
              "4          1              1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10d580c5-03d1-40d4-a1e9-3254e21426cd\">\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>timestamp</th>\n",
              "      <th>FQDN_count</th>\n",
              "      <th>subdomain_length</th>\n",
              "      <th>upper</th>\n",
              "      <th>lower</th>\n",
              "      <th>numeric</th>\n",
              "      <th>entropy</th>\n",
              "      <th>special</th>\n",
              "      <th>labels</th>\n",
              "      <th>labels_max</th>\n",
              "      <th>labels_average</th>\n",
              "      <th>longest_word</th>\n",
              "      <th>sld</th>\n",
              "      <th>len</th>\n",
              "      <th>subdomain</th>\n",
              "      <th>Target Attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56:19.8</td>\n",
              "      <td>27</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>2.570417</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>07:23.9</td>\n",
              "      <td>27</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>2.767195</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>23:15.1</td>\n",
              "      <td>26</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>2.742338</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>04:51.9</td>\n",
              "      <td>27</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>2.570417</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>2</td>\n",
              "      <td>192</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12:44.0</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2.929439</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>local</td>\n",
              "      <td>local</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10d580c5-03d1-40d4-a1e9-3254e21426cd')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-10d580c5-03d1-40d4-a1e9-3254e21426cd 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-10d580c5-03d1-40d4-a1e9-3254e21426cd');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8853-eOmqmwk",
        "outputId": "99aea602-e425-4cf4-cc23-97d9b0fd3a5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 268074 entries, 0 to 268073\n",
            "Data columns (total 16 columns):\n",
            " #   Column            Non-Null Count   Dtype  \n",
            "---  ------            --------------   -----  \n",
            " 0   timestamp         268074 non-null  object \n",
            " 1   FQDN_count        268074 non-null  int64  \n",
            " 2   subdomain_length  268074 non-null  int64  \n",
            " 3   upper             268074 non-null  int64  \n",
            " 4   lower             268074 non-null  int64  \n",
            " 5   numeric           268074 non-null  int64  \n",
            " 6   entropy           268074 non-null  float64\n",
            " 7   special           268074 non-null  int64  \n",
            " 8   labels            268074 non-null  int64  \n",
            " 9   labels_max        268074 non-null  int64  \n",
            " 10  labels_average    268074 non-null  float64\n",
            " 11  longest_word      268066 non-null  object \n",
            " 12  sld               268074 non-null  object \n",
            " 13  len               268074 non-null  int64  \n",
            " 14  subdomain         268074 non-null  int64  \n",
            " 15  Target Attack     268074 non-null  int64  \n",
            "dtypes: float64(2), int64(11), object(3)\n",
            "memory usage: 32.7+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "w2WFe2wwgTcI",
        "outputId": "b86d0c5e-ee24-4d5a-c51a-1f8eb6cb25d4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          FQDN_count  subdomain_length          upper          lower  \\\n",
              "count  268074.000000     268074.000000  268074.000000  268074.000000   \n",
              "mean       22.286596          6.059021       0.845420      10.410014   \n",
              "std         6.001205          3.899505       4.941929       3.207725   \n",
              "min         2.000000          0.000000       0.000000       0.000000   \n",
              "25%        18.000000          3.000000       0.000000      10.000000   \n",
              "50%        24.000000          7.000000       0.000000      10.000000   \n",
              "75%        27.000000         10.000000       0.000000      10.000000   \n",
              "max        36.000000         23.000000      32.000000      34.000000   \n",
              "\n",
              "             numeric        entropy        special         labels  \\\n",
              "count  268074.000000  268074.000000  268074.000000  268074.000000   \n",
              "mean        6.497586       2.485735       4.533577       4.788823   \n",
              "std         4.499866       0.407709       2.187683       1.803256   \n",
              "min         0.000000       0.219195       0.000000       1.000000   \n",
              "25%         0.000000       2.054029       2.000000       3.000000   \n",
              "50%         8.000000       2.570417       6.000000       6.000000   \n",
              "75%        10.000000       2.767195       6.000000       6.000000   \n",
              "max        12.000000       4.216847       7.000000       7.000000   \n",
              "\n",
              "          labels_max  labels_average            len      subdomain  \\\n",
              "count  268074.000000   268074.000000  268074.000000  268074.000000   \n",
              "mean        8.252233        4.802239      12.576714       0.753497   \n",
              "std         4.415355        4.573066       4.177828       0.430975   \n",
              "min         2.000000        2.000000       2.000000       0.000000   \n",
              "25%         7.000000        3.166667      11.000000       1.000000   \n",
              "50%         7.000000        3.666667      12.000000       1.000000   \n",
              "75%         7.000000        4.000000      14.000000       1.000000   \n",
              "max        32.000000       32.000000      33.000000       1.000000   \n",
              "\n",
              "       Target Attack  \n",
              "count  268074.000000  \n",
              "mean        0.549024  \n",
              "std         0.497592  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         1.000000  \n",
              "75%         1.000000  \n",
              "max         1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12e44eb0-fe3e-46cb-9140-f7adc4234ad4\">\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>FQDN_count</th>\n",
              "      <th>subdomain_length</th>\n",
              "      <th>upper</th>\n",
              "      <th>lower</th>\n",
              "      <th>numeric</th>\n",
              "      <th>entropy</th>\n",
              "      <th>special</th>\n",
              "      <th>labels</th>\n",
              "      <th>labels_max</th>\n",
              "      <th>labels_average</th>\n",
              "      <th>len</th>\n",
              "      <th>subdomain</th>\n",
              "      <th>Target Attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "      <td>268074.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.286596</td>\n",
              "      <td>6.059021</td>\n",
              "      <td>0.845420</td>\n",
              "      <td>10.410014</td>\n",
              "      <td>6.497586</td>\n",
              "      <td>2.485735</td>\n",
              "      <td>4.533577</td>\n",
              "      <td>4.788823</td>\n",
              "      <td>8.252233</td>\n",
              "      <td>4.802239</td>\n",
              "      <td>12.576714</td>\n",
              "      <td>0.753497</td>\n",
              "      <td>0.549024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.001205</td>\n",
              "      <td>3.899505</td>\n",
              "      <td>4.941929</td>\n",
              "      <td>3.207725</td>\n",
              "      <td>4.499866</td>\n",
              "      <td>0.407709</td>\n",
              "      <td>2.187683</td>\n",
              "      <td>1.803256</td>\n",
              "      <td>4.415355</td>\n",
              "      <td>4.573066</td>\n",
              "      <td>4.177828</td>\n",
              "      <td>0.430975</td>\n",
              "      <td>0.497592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.219195</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.054029</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.166667</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.570417</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.767195</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>4.216847</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12e44eb0-fe3e-46cb-9140-f7adc4234ad4')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-12e44eb0-fe3e-46cb-9140-f7adc4234ad4 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-12e44eb0-fe3e-46cb-9140-f7adc4234ad4');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "l8L3L6-MmPbr",
        "outputId": "c3a25e20-7a7b-43eb-f32e-7744c4bb39c7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  FQDN_count  subdomain_length     upper     lower   numeric  \\\n",
              "FQDN_count          1.000000          0.788615  0.223162 -0.095881  0.793079   \n",
              "subdomain_length    0.788615          1.000000 -0.265780 -0.073809  0.944548   \n",
              "upper               0.223162         -0.265780  1.000000 -0.555147 -0.236155   \n",
              "lower              -0.095881         -0.073809 -0.555147  1.000000 -0.197667   \n",
              "numeric             0.793079          0.944548 -0.236155 -0.197667  1.000000   \n",
              "entropy             0.167806         -0.015773  0.115458  0.385455 -0.084281   \n",
              "special             0.748354          0.929076 -0.347061 -0.068635  0.941951   \n",
              "labels              0.739786          0.923210 -0.359428 -0.057215  0.940191   \n",
              "labels_max          0.186992         -0.410820  0.847046 -0.074632 -0.395441   \n",
              "labels_average      0.109444         -0.431748  0.946883 -0.318585 -0.411193   \n",
              "len                 0.588849          0.090472  0.759096 -0.072903  0.037743   \n",
              "subdomain           0.698685          0.888718 -0.299079  0.036077  0.801602   \n",
              "Target Attack       0.563563          0.572053  0.072895 -0.295599  0.599252   \n",
              "\n",
              "                   entropy   special    labels  labels_max  labels_average  \\\n",
              "FQDN_count        0.167806  0.748354  0.739786    0.186992        0.109444   \n",
              "subdomain_length -0.015773  0.929076  0.923210   -0.410820       -0.431748   \n",
              "upper             0.115458 -0.347061 -0.359428    0.847046        0.946883   \n",
              "lower             0.385455 -0.068635 -0.057215   -0.074632       -0.318585   \n",
              "numeric          -0.084281  0.941951  0.940191   -0.395441       -0.411193   \n",
              "entropy           1.000000 -0.192317 -0.226686    0.418591        0.307460   \n",
              "special          -0.192317  1.000000  0.991309   -0.477689       -0.525847   \n",
              "labels           -0.226686  0.991309  1.000000   -0.497794       -0.545609   \n",
              "labels_max        0.418591 -0.477689 -0.497794    1.000000        0.954819   \n",
              "labels_average    0.307460 -0.525847 -0.545609    0.954819        1.000000   \n",
              "len               0.526296 -0.070203 -0.096721    0.856082        0.802346   \n",
              "subdomain        -0.138240  0.890508  0.898859   -0.420510       -0.473714   \n",
              "Target Attack    -0.119050  0.582107  0.569414   -0.096350       -0.077167   \n",
              "\n",
              "                       len  subdomain  Target Attack  \n",
              "FQDN_count        0.588849   0.698685       0.563563  \n",
              "subdomain_length  0.090472   0.888718       0.572053  \n",
              "upper             0.759096  -0.299079       0.072895  \n",
              "lower            -0.072903   0.036077      -0.295599  \n",
              "numeric           0.037743   0.801602       0.599252  \n",
              "entropy           0.526296  -0.138240      -0.119050  \n",
              "special          -0.070203   0.890508       0.582107  \n",
              "labels           -0.096721   0.898859       0.569414  \n",
              "labels_max        0.856082  -0.420510      -0.096350  \n",
              "labels_average    0.802346  -0.473714      -0.077167  \n",
              "len               1.000000   0.013307       0.159437  \n",
              "subdomain         0.013307   1.000000       0.502469  \n",
              "Target Attack     0.159437   0.502469       1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-303f1ba5-121f-4f72-b206-22e01c9281b1\">\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>FQDN_count</th>\n",
              "      <th>subdomain_length</th>\n",
              "      <th>upper</th>\n",
              "      <th>lower</th>\n",
              "      <th>numeric</th>\n",
              "      <th>entropy</th>\n",
              "      <th>special</th>\n",
              "      <th>labels</th>\n",
              "      <th>labels_max</th>\n",
              "      <th>labels_average</th>\n",
              "      <th>len</th>\n",
              "      <th>subdomain</th>\n",
              "      <th>Target Attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>FQDN_count</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.788615</td>\n",
              "      <td>0.223162</td>\n",
              "      <td>-0.095881</td>\n",
              "      <td>0.793079</td>\n",
              "      <td>0.167806</td>\n",
              "      <td>0.748354</td>\n",
              "      <td>0.739786</td>\n",
              "      <td>0.186992</td>\n",
              "      <td>0.109444</td>\n",
              "      <td>0.588849</td>\n",
              "      <td>0.698685</td>\n",
              "      <td>0.563563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subdomain_length</th>\n",
              "      <td>0.788615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.265780</td>\n",
              "      <td>-0.073809</td>\n",
              "      <td>0.944548</td>\n",
              "      <td>-0.015773</td>\n",
              "      <td>0.929076</td>\n",
              "      <td>0.923210</td>\n",
              "      <td>-0.410820</td>\n",
              "      <td>-0.431748</td>\n",
              "      <td>0.090472</td>\n",
              "      <td>0.888718</td>\n",
              "      <td>0.572053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>upper</th>\n",
              "      <td>0.223162</td>\n",
              "      <td>-0.265780</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.555147</td>\n",
              "      <td>-0.236155</td>\n",
              "      <td>0.115458</td>\n",
              "      <td>-0.347061</td>\n",
              "      <td>-0.359428</td>\n",
              "      <td>0.847046</td>\n",
              "      <td>0.946883</td>\n",
              "      <td>0.759096</td>\n",
              "      <td>-0.299079</td>\n",
              "      <td>0.072895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lower</th>\n",
              "      <td>-0.095881</td>\n",
              "      <td>-0.073809</td>\n",
              "      <td>-0.555147</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.197667</td>\n",
              "      <td>0.385455</td>\n",
              "      <td>-0.068635</td>\n",
              "      <td>-0.057215</td>\n",
              "      <td>-0.074632</td>\n",
              "      <td>-0.318585</td>\n",
              "      <td>-0.072903</td>\n",
              "      <td>0.036077</td>\n",
              "      <td>-0.295599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numeric</th>\n",
              "      <td>0.793079</td>\n",
              "      <td>0.944548</td>\n",
              "      <td>-0.236155</td>\n",
              "      <td>-0.197667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.084281</td>\n",
              "      <td>0.941951</td>\n",
              "      <td>0.940191</td>\n",
              "      <td>-0.395441</td>\n",
              "      <td>-0.411193</td>\n",
              "      <td>0.037743</td>\n",
              "      <td>0.801602</td>\n",
              "      <td>0.599252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>entropy</th>\n",
              "      <td>0.167806</td>\n",
              "      <td>-0.015773</td>\n",
              "      <td>0.115458</td>\n",
              "      <td>0.385455</td>\n",
              "      <td>-0.084281</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.192317</td>\n",
              "      <td>-0.226686</td>\n",
              "      <td>0.418591</td>\n",
              "      <td>0.307460</td>\n",
              "      <td>0.526296</td>\n",
              "      <td>-0.138240</td>\n",
              "      <td>-0.119050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>special</th>\n",
              "      <td>0.748354</td>\n",
              "      <td>0.929076</td>\n",
              "      <td>-0.347061</td>\n",
              "      <td>-0.068635</td>\n",
              "      <td>0.941951</td>\n",
              "      <td>-0.192317</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.991309</td>\n",
              "      <td>-0.477689</td>\n",
              "      <td>-0.525847</td>\n",
              "      <td>-0.070203</td>\n",
              "      <td>0.890508</td>\n",
              "      <td>0.582107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>labels</th>\n",
              "      <td>0.739786</td>\n",
              "      <td>0.923210</td>\n",
              "      <td>-0.359428</td>\n",
              "      <td>-0.057215</td>\n",
              "      <td>0.940191</td>\n",
              "      <td>-0.226686</td>\n",
              "      <td>0.991309</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.497794</td>\n",
              "      <td>-0.545609</td>\n",
              "      <td>-0.096721</td>\n",
              "      <td>0.898859</td>\n",
              "      <td>0.569414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>labels_max</th>\n",
              "      <td>0.186992</td>\n",
              "      <td>-0.410820</td>\n",
              "      <td>0.847046</td>\n",
              "      <td>-0.074632</td>\n",
              "      <td>-0.395441</td>\n",
              "      <td>0.418591</td>\n",
              "      <td>-0.477689</td>\n",
              "      <td>-0.497794</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.954819</td>\n",
              "      <td>0.856082</td>\n",
              "      <td>-0.420510</td>\n",
              "      <td>-0.096350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>labels_average</th>\n",
              "      <td>0.109444</td>\n",
              "      <td>-0.431748</td>\n",
              "      <td>0.946883</td>\n",
              "      <td>-0.318585</td>\n",
              "      <td>-0.411193</td>\n",
              "      <td>0.307460</td>\n",
              "      <td>-0.525847</td>\n",
              "      <td>-0.545609</td>\n",
              "      <td>0.954819</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.802346</td>\n",
              "      <td>-0.473714</td>\n",
              "      <td>-0.077167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>len</th>\n",
              "      <td>0.588849</td>\n",
              "      <td>0.090472</td>\n",
              "      <td>0.759096</td>\n",
              "      <td>-0.072903</td>\n",
              "      <td>0.037743</td>\n",
              "      <td>0.526296</td>\n",
              "      <td>-0.070203</td>\n",
              "      <td>-0.096721</td>\n",
              "      <td>0.856082</td>\n",
              "      <td>0.802346</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013307</td>\n",
              "      <td>0.159437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subdomain</th>\n",
              "      <td>0.698685</td>\n",
              "      <td>0.888718</td>\n",
              "      <td>-0.299079</td>\n",
              "      <td>0.036077</td>\n",
              "      <td>0.801602</td>\n",
              "      <td>-0.138240</td>\n",
              "      <td>0.890508</td>\n",
              "      <td>0.898859</td>\n",
              "      <td>-0.420510</td>\n",
              "      <td>-0.473714</td>\n",
              "      <td>0.013307</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.502469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Target Attack</th>\n",
              "      <td>0.563563</td>\n",
              "      <td>0.572053</td>\n",
              "      <td>0.072895</td>\n",
              "      <td>-0.295599</td>\n",
              "      <td>0.599252</td>\n",
              "      <td>-0.119050</td>\n",
              "      <td>0.582107</td>\n",
              "      <td>0.569414</td>\n",
              "      <td>-0.096350</td>\n",
              "      <td>-0.077167</td>\n",
              "      <td>0.159437</td>\n",
              "      <td>0.502469</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-303f1ba5-121f-4f72-b206-22e01c9281b1')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-303f1ba5-121f-4f72-b206-22e01c9281b1 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-303f1ba5-121f-4f72-b206-22e01c9281b1');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.skew()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmeJOB3ihMx4",
        "outputId": "11ef3f14-ddaf-40c5-9160-aebcec8921b2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FQDN_count         -1.101731\n",
              "subdomain_length   -0.590480\n",
              "upper               5.988737\n",
              "lower               0.343449\n",
              "numeric            -0.594384\n",
              "entropy            -0.140156\n",
              "special            -0.902972\n",
              "labels             -0.903680\n",
              "labels_max          3.979910\n",
              "labels_average      5.087081\n",
              "len                 2.634801\n",
              "subdomain          -1.176397\n",
              "Target Attack      -0.197046\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['labels_max'].plot.kde()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "C2K5_ppvhbX7",
        "outputId": "a611e3b4-aa5c-4477-b814-211770ab85af"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa14675df10>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['labels_max'].plot(kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Y_17O86hhgUg",
        "outputId": "ac017445-77ec-42b1-e889-2234d67eee16"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1439edad0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['FQDN_count'].plot(kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "u3c09JEwr6nd",
        "outputId": "730bb3f9-3fe5-409f-bcd9-2e8edecc2183"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa14398eed0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['FQDN_count'].plot.kde()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "yATnB0IiuMJc",
        "outputId": "bb38b3f8-33aa-4c91-a36e-542c9531cf8b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa143905c10>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD4CAYAAAD2FnFTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZxcZZX/8c+prbuzdPYEyEISCEKUQKCNCAqoIIgOiNuACzjDTxxHfj8dHB3U36CDMzo64zILKjgyOswoIm5RGZFVHERIswohgSSELISkSdLpJL3VcuaPutWp7r61JPTtrpt8369Xv9J1697qQ+jUqfOc53muuTsiIiJDJcY6ABERaUxKECIiEkoJQkREQilBiIhIKCUIEREJlRrrAEbK9OnTff78+WMdhohIrDz00EMvuvuMsOcOmgQxf/582tvbxzoMEZFYMbPnKj2nISYREQmlBCEiIqGUIEREJJQShIiIhFKCEBGRUEoQIiISSglCRERCKUGIxFRfLs/3HthAbzY/1qHIQeqgWSgncqj55zuf4dq719KcTvC2k+aMdThyEFIFIRJTWzp7Adi2u2+MI5GDlRKESEz15QoAbN7ZM8aRyMFKCUIkprbvLVYOnT3ZMY5EDlZKECIxtXNvMTF0KUFIRJQgRGJqb38OgN29ShASDSUIkZjq6S9Ob93dmxvjSORgpQQhElOlCqJLFYRERAlCJIbyBac3W5zFVKokREaaEoRIDHUH1UM6aQOJQmSkKUGIxFB3UDVMHZ+hP18gl1eSkJGnBCESQ6X9l6aMyxQf55QgZOQpQYjEUGkVdWtLGkAb9kkklCBEYqg/SBCTggShRrVEQQlCJIb6hiQIVRASBSUIkRjqyxUTwuSBBKEehIw8JQiRGBo2xFRnBdGfK7Dqha7I4pKDixKESAwNbVLXmyC+/+AGzv3ab/nN0x2RxSYHDyUIkRgqVRCTx+1fk/qJzbsAePL5XdEEJgeVSBOEmZ1rZqvNbI2ZXRXy/JVmttLMHjezO83syLLnLjWzZ4KvS6OMUyRu+odUEKWeRC2le0d06C50UofIEoSZJYFrgTcBi4GLzWzxkNMeAdrcfQlwC/Cl4NqpwGeAVwHLgM+Y2ZSoYhWJm6GzmOqtIHZ1FxOEblMq9YiyglgGrHH3de7eD9wEXFB+grvf7e7dwcPfA6U7r58D3O7uO9x9J3A7cG6EsYrESn9QMexvk3p3X3EPp1KiEKkmygQxG9hY9nhTcKySy4D/3p9rzexyM2s3s/aODjXd5NDRnz+wWUylmwvt6dM9JKS2hmhSm9l7gTbgH/bnOne/3t3b3L1txowZ0QQn0oD6gnUPrc3BOog6h5hKiUEJQuoRZYLYDMwtezwnODaImZ0FfBo439379udakUNVf76AWXG776ZUYqAnUY27sye4+9xeJQipQ5QJYgWwyMwWmFkGuAhYXn6CmS0FrqOYHLaVPXUb8EYzmxI0p98YHBMRirOYMskEZvUniL5cgVzBAVUQUp9UVC/s7jkzu4LiG3sSuMHdnzSza4B2d19OcUhpAvBDMwPY4O7nu/sOM/scxSQDcI2774gqVpG46csVaEoVP981pZN1TXMt3bt68rg0XT1Z3J3g351IqMgSBIC73wrcOuTY1WXfn1Xl2huAG6KLTiS++nIFMqkkAM3pxEBPoppS1TBjQhOd3Vn68wWagtcQCdMQTWoR2T/95RVEKlnXEFNpBtP0CU0AdV0jhzYlCJEY6svlyxJEoq4hplKDetqE4C502iJcalCCEImh/lyBTFmCqGe779IiuWnjiwminmEpObQpQYjEUH++PEHU16QeuI91KUFoiElqUIIQiaHSNFeApnR901xLCWLSfm7wJ4cuJQiRGMrlnVSyOEW1KVXfLKbeoauvNcQkNShBiMRQtlAgHVQQzXWug1AFIftLCUIkhnJ5J5UoqyDqGmIKNvgbV0oQqiCkOiUIkRjK5vdVEPWug+jL5UkljPGZ4vpYzWKSWpQgRGJocIJI1LWmoTdboDmdpCldvE5DTFKLEoRIDOUKZU3qemcx5fI0pxMDC+xUQUgtShAiMVTsQewbYsoXnFy++ht+bzZPUyo5sP+SKgipRQlCJIaKQ0z7mtRQu+ncly3QnE7QnK7vfBElCJEYKh9iak6XKoL9qyC0F5PUogQhEkPZ3OAmNdQeMir1INJJw0wVhNSmBCESQ+UL5QZmJdVoOpdmMZkZzXVOjZVDmxKESAwNXigXDBnVqiCy+YHhqKZ0gj4NMUkNShAiMePuQQ9iyBBTjQqiL1cYaFDXu0W4HNqUIERiJldwANJDKoh6mtTNwbmZVIJsjWmxIkoQIjFTemNPp4b0IGoOMRVoCoaY0skE/UoQUoMShEjMZPPFCqLUgyhVBTWHmLL5gSGmTFIVhNSmBCESM6UV08NmMdUaYsrlB4aj0snEQKIRqUQJQiRmSj2I1LCV1JWHmPIFJ5v3gQoilTRVEFKTEoRIzAz0IMr2YoLqd4grrZpuLu9BaB2E1KAEIRIzufz+VxADCSKlHoTUTwlCJGayB9CDKD23r4Iw9SCkJiUIkZgpvbGXdnPNJGsvlAsbYlIFIbUoQYjETK5QfGMv3Q8ilUyQSliNIaZSBVG8Jq2FclIHJQiRmMkO6UFAsTKoNsRU2qeptFAuo2muUgclCJGYGboOAoqN6vqa1OU9CFUQUp0ShEjM7OtBDEkQVXoQpedKDW31IKQeShAiMZMt9SDKhpia0kl6qw0xDasgtA5CalOCEImZ0jqI0kI5KFUQVYaYcqVZTME6iJR6EFKbEoRIzJR6EIMqiFSi+jqIbNg6CFUQUp0ShEjMZAuD10FAcbuNuprUZesgcgWnUFAVIZVFmiDM7FwzW21ma8zsqpDnTzezh80sZ2bvGPJc3sweDb6WRxmnSJwMVBDlQ0zp6hVEb27IOoigwV3qZ4iESUX1wmaWBK4FzgY2ASvMbLm7ryw7bQPwfuAvQ16ix91PjCo+kbgaesMgKFYQ2/f0V7xmaJO6tPo6m3eaInsXkLiL8ldjGbDG3dcBmNlNwAXAQIJw9/XBc/oYI1KngWmuifJZTLXWQRRIJ41EcE2pf5HNFaApwmAl1qIcYpoNbCx7vCk4Vq9mM2s3s9+b2VvDTjCzy4Nz2js6Ol5KrCKxsa9JPXgWU63tvkv9BygbYlKjWqpo5Cb1ke7eBrwb+JqZHTX0BHe/3t3b3L1txowZox+hyBgYesMgKDWpqyeIlrIEURpi0n2ppZooE8RmYG7Z4znBsbq4++bgz3XAPcDSkQxOJK6yldZB1JjF1JIpqyBSNui1RMJEmSBWAIvMbIGZZYCLgLpmI5nZFDNrCr6fDpxGWe9C5FAWug6ixiymnmx+oEEN+4aYcqogpIrIEoS754ArgNuAp4Cb3f1JM7vGzM4HMLNXmtkm4J3AdWb2ZHD5cUC7mT0G3A38/ZDZTyKHrOzANNey3VxTSfpzBdzDK4KebIHmzPAEoSEmqSbSCW7ufitw65BjV5d9v4Li0NPQ634HHB9lbCJxlS046aRhNriCgOKd48qb0SW9/Xla0vs+D5ZPcxWppJGb1CISIpcvDFokB8UmNVS+7WhvTrOYZP8pQYjETDbvg/oPUGxSAxU37OvpHzyLKV2+DkKkAiUIkZjJFQqD7gUBZQmiwht+z5BprqVV2OpBSDVKECIxk835oI36YN+tRCtNde0d0qRWD0LqoQQhEjPZQlgPovi40mrq3grTXNWDkGqUIERiJpcfXkE0pys3qd29OMSU2ffPfaAHoQQhVShBiMRMrlAYtA8TlPcghg8xZfNOvuBDmtRBD0JNaqlCCUIkZrJ5H7RIDqo3qffdbjRsiEk9CKlMCUIkZnL5sFlMwRBTyDTX3v6wBKEhJqlNCUIkZrKhPYjKTeqeIGmETXNVgpBqlCBEYiabH96DGJcp7prT3R9SQQRJoyVkmqvWQUg1dSUIM/uxmb3ZzJRQRMZYrjC8gii9+Xf354adX6ogmtPls5hKu7mqByGV1fuG/3WKN+55xsz+3sxeFmFMIlJF2F5M4wYSxPAKoiekB5FMGAnTEJNUV1eCcPc73P09wEnAeuAOM/udmf2JmaWjDFBEBgvrQaSTCTLJBHtDKojekB5E6RpNc5Vq6h4yMrNpwPuB/wM8AvwTxYRxeySRiUiobMgsJoBxTcmBaqFcb3Z4BQHFPoSmuUo1dd0Pwsx+ArwMuBH4I3ffEjz1AzNrjyo4ERkuV/BhTWqAcekke/tChpgqVRCphIaYpKp6bxj0reDmPwPMrMnd+9y9LYK4RKSCbL5AeshCOYBxTSl6spWb1OWzmKC4FkIJQqqpd4jpb0OO3T+SgYhIfXIh94OAYqM6rIIoTXMt36wPgh6EEoRUUbWCMLPDgNlAi5ktBUq/la3AuIhjE5EQYXsxQTFBhPUguvuKVcXQCkI9CKml1hDTORQb03OAr5Qd3w18KqKYRKSKbN7Dh5gyKbZ29Q47vqcvRyaVIJManFTSyYTuKCdVVU0Q7v5d4Ltm9nZ3/9EoxSQiVYStpIbKFcSevhwTmob/U0+nTENMUlWtIab3uvt/AvPN7Mqhz7v7V0IuE5EIFe8HEZ4gwtZB7O3LMb4pOex4OqlZTFJdrSGm8cGfE6IORERqc3eyhQKZ0CZ1iu6QJvWevjzjMyEVhBbKSQ21hpiuC/78m9EJR0SqyRccd0KHmCY0pdjbn8PdMduXQPZWGGLKJBOhezeJlNS7Wd+XzKzVzNJmdqeZdZjZe6MOTkQGyxWKs47ChphaW1IUHPYO6UPs7c8xPqwHkTTNYpKq6l0H8UZ37wLeQnEvpqOBj0cVlIiEKzWVh+7FBDCxubgtWldPdtDxik1q9SCkhnoTROm3683AD919V0TxiEgVpe25wyqIic3Ff6a7ewcPG1VsUqe0UE6qq3erjV+Y2SqgB/iQmc0Ahk+4FpFIlT7xh62kbg0qiN29QyqI3hwTmoZvupxRBSE11Lvd91XAqUCbu2eBvcAFUQYmIsNlB4aYKlcQXWUJolBw9vbnmRA6zdXI5tSDkMrqrSAAjqW4HqL8mv8Y4XhEpIrswBBT5R5E+RBTd7BRX3iTWhWEVFfvdt83AkcBjwKlKRKOEoTIqMpVqSBaW0oVxL4EUWpYt7aEDDGpByE11FtBtAGL3V31qMgYKr2hD73lKOzrQZTPYursLn4/OSxBqIKQGuqdxfQEcFiUgYhIbaVZTJnU8CGmplTxtqPlQ0ydPf0ATBo3PEGktZur1FBvBTEdWGlmDwJ9pYPufn4kUYlIqGyVCsLMaG1JsytICgC7ggpiyrjMsPPTyQT5gpMvOMmQ3WFF6k0Qn40yCBGpT7bKOgiAqePT7Ni7L0F0BsNNk8MqiKAKyeYLJBPDZzmJ1DvN9TcUV1Cng+9XAA/Xus7MzjWz1Wa2xsyuCnn+dDN72MxyZvaOIc9dambPBF+X1vVfI3KQy1ZZSQ0wdXxmUILYVUoQLcMriEyQZNSHkErq3YvpA8AtwHXBodnAT2tckwSuBd4ELAYuNrPFQ07bQPGGRN8bcu1U4DPAq4BlwGfMbEo9sYoczHKF0kK58H+608Y3Da4gurNkUgma08PPTw8kCPUhJFy9TeoPA6cBXQDu/gwws8Y1y4A17r7O3fuBmxiyuM7d17v748DQjzDnALe7+w533wncDpxbZ6wiB63+XOV1EABThgwx7erpZ3JLetDuriVpVRBSQ70Joi94kwcgWCxX62PHbGBj2eNNwbF61HWtmV1uZu1m1t7R0VHnS4vEV6mCqNyDaKKzJ0s+2PV1595saP+h+BrFpKF7Qkgl9SaI35jZp4AWMzsb+CHw8+jCqo+7X+/ube7eNmPGjLEORyRy1bbaAJg2PoM7dHYXP89t293LzInNoeeW7lGtCkIqqTdBXAV0AH8APgjcCvz/GtdsBuaWPZ4THKvHS7lW5KBV6hekKkxLnTq+2IzeHgwzbe3qY+bEptBz1YOQWuqa5uruBTP7KfBTd693LGcFsMjMFlB8c78IeHed194GfL6sMf1G4JN1Xity0Cp92i99+h/qsEnFamHLrl4WzZxQrCBawysI9SCklqoVhBV91sxeBFYDq4O7yV1d64XdPQdcQfHN/ingZnd/0syuMbPzg9d/pZltAt4JXGdmTwbX7gA+RzHJrACuCY6JHNJyNSqIw0sJorOHnd1ZsnmvUkEEPQglCKmgVgXxFxRnL73S3Z8FMLOFwDfM7C/c/avVLnb3WykOR5Ufu7rs+xUUh4/Crr0BuKHmf4HIIWSgB1GhgpjV2owZPN/Zw7bdvQPHwgysg1CTWiqo1YN4H3BxKTkAuPs64L3AJVEGJiLDDaykDtlqA4rDRrMmNvP8rl627ColiAoVREo9CKmuVoJIu/uLQw8GfYjwuXMiEplcjZXUAIdPbub5zh7WbtsDwILp40PPUw9CaqmVIPoP8DkRiUDpzbza5noLp0/gmW17WNuxhynj0kyboB6EHJhaCeIEM+sK+doNHD8aAYrIPtmCk0kmQldGl7xidisdu/u4b812jp45oeJ52otJaqnapHZ3bfEo0kCyuQKpKsNLAC8/YhIAG3Z0c/ox0yuepyEmqaXehXIi0gByBa+4irrkFbNbB74/85jKW6YNNKlzalJLuHrvByEiDaA/X6jaoAYYl0nx7UvbeGzTLl5/bJUEoR6E1KAEIRIjuXyhZgUB8IbjZvGG42ZVPUc9CKlFQ0wiMZLNe80eRL3Ug5BalCBEYiRbZwVRD23WJ7UoQYjESDZfqLiKen/pfhBSixKESIzk8k46NTJDTGZGOmkaYpKKlCBEYqQ/XyA1QhUEFIeZlCCkEiUIkRjJ5b3mNNf9UUwQ6kFIOCUIkRgZySY1FBOE1kFIJUoQIjGSLTipEUwQmaTpfhBSkRKESIxkcwUyIznElFIPQipTghCJkVwhiia1ehASTglCJEayea94u9EDkU4m6NMQk1SgBCESI/252pv17Y+M1kFIFUoQIjHSny/QNMIVhFZSSyVKECIx0p8rDOzCOhIyalJLFUoQIjHSl8uTGcEKoimlHoRUpgQhEiP9uQJNqZG7E3BTKklfLj9irycHFyUIkZjI5QsUnJGtINKqIKQyJQiRmChtiTGSCaI5laQvqwQh4ZQgRGKiNNtoJJvUxQpCQ0wSTglCJCZKQ0FNaTWpZXQoQYjERCQVRCqpBCEVKUGIxETpjXykp7nmC05OayEkhBKESEyUKoiRXEldGq5SFSFhlCBEYiKKWUylNRW9WTWqZTglCJGY6AvexEd2oZwqCKlMCUIkJiKpIDTEJFUoQYjERFSzmACthZBQkSYIMzvXzFab2Rozuyrk+SYz+0Hw/ANmNj84Pt/Meszs0eDrm1HGKRIH/RHNYgK0mlpCpaJ6YTNLAtcCZwObgBVmttzdV5addhmw092PNrOLgC8Cfxw8t9bdT4wqPpG4ibJJrSEmCRNlBbEMWOPu69y9H7gJuGDIORcA3w2+vwV4g5mN3O2yRA4ipU/50Uxz1RCTDBdlgpgNbCx7vCk4FnqOu+eAXcC04LkFZvaImf3GzF4b9gPM7HIzazez9o6OjpGNXqTB9EW0WR9oiEnCNWqTegswz92XAlcC3zOz1qEnufv17t7m7m0zZswY9SBFRtPAQrnkCE5z1SwmqSLKBLEZmFv2eE5wLPQcM0sBk4Dt7t7n7tsB3P0hYC1wTISxijS8KJvUPVooJyGiTBArgEVmtsDMMsBFwPIh5ywHLg2+fwdwl7u7mc0ImtyY2UJgEbAuwlhFGl7vwEK5kftn25IpViNKEBImsllM7p4zsyuA24AkcIO7P2lm1wDt7r4c+DZwo5mtAXZQTCIApwPXmFkWKAB/5u47oopVJA56s3ma0wkSiZGbxzEuU3wL6O7LjdhrysEjsgQB4O63ArcOOXZ12fe9wDtDrvsR8KMoYxOJm+7+PC3pkes/AAOv192vCkKGa9QmtYgM0ZMd+QSRTBhNqYSGmCSUEoRITPRk8zRnRjZBAIzLJOnu1xCTDKcEIRITvREMMUGxD6EhJgmjBCESE1EMMUGxguhRgpAQShAiMdGTzQ9MSx1JxSEmJQgZTglCJCZ6IhpialEFIRUoQYjERG9kFUSKvWpSSwglCJGYiKoHoQpCKlGCEImJnv48zVE0qdPqQUg4JQiREJt2dnPZd1bwsZsfG9gDaaz1ZgsRNqk1xCTDRbrVhkgc9eXyXPaddta9uIds3mlKJ/j8hcePaUy5fIH+fCGSIabxTcV1EO6O7tcl5VRBiAzx1dufYfXW3Vz/vjY+8NoFfO+BDTz03M4xjak32Oo7igTR2pImV3BttyHDKEGIBLr7c3zh1qe47t61XPTKubzu2Jl89KxjOKy1mb/+6RPkCz5mse0NdluNYoiptTkNQFePhplkMCUIOSRt7uzhkQ07yQW38fzN0x288av3ct296/jjtrlc/UeLgeLwy1+/ZTErt3Rx3b1rxyze3b1ZACa1pEf8tVtbiiPNu3qyI/7aEm/qQcghpVBwbrjvWb70q9X05wtMG59h4YzxrFi/k6NmjOfmD76aZQumDrrmvOMP481LDudLv1rNnCnjOP+EI0Y97l3Bp/uJzSP/T7aUdLp6lSBkMCUIOej87NHN3L92OyfMncySOZO4Y+U27nhqK2bFoZq1HXs5e/Es3nz84dy+ciurXujiyrOP4YNnLKQpNXwIx8z48jtPoKOrj4/d/CgzJjTx6qOmjep/U+nNuzWKCmJgiEkJQgZTgpCDRr7gfPFXq7j+3nVkkgluWrFx4Lll86fSkknSkk7y52cezdtOmo2Z8dals+t67eZ0km9d0saF37iPv/zhY9xx5RmR9AMq2d1brCBaI6wgNMQkQylByEHhoed28pXbV3Pfmu1c8uojufoti1nTsYfHN+6ibf4UFs6Y8JJ/xqRxaT5/4fFcdP3vue7etXz0rGNGIPL6lD7dlz7tj6RSVaIKQoZSgpBY29OX46M3PcodT21lyrg0n3vrK3jfKUcCcOxhrRx7WOuI/rxTFk7jLUsO5xv3rOXtJ81h7tRxI/r6lUQ7xJQKfoZmMclgmsUksfa5n6/krlVb+atzj+W+q14/kByi9KnzjiNhxt/98qnIf1bJ7t4c6WTx9qAjLZVMMD6T1BCTDKMEIbF19+pt/KB9I5effhQfOvMoxmVGpyA+YnILV7z+aH715Av83S9XUhiF9RFdPVlam9ORrXSeOiHD9j19B3x9Nl/gYzc/xoe/93DDbE0iL52GmCSWdnVnuepHj7No5gQ+etaiUf/5HzrjKLZ19fKt3z7L8529fPldJ0SykV5JV28ukimuJTMnNrNt94EniP9+4gV+9PAmAE6eN4U/fc2CkQpNxpAqCImd3myey29sZ/ue/sjfmCtJJIzPnv9yPnXesfzyD1u4/MaHIv3kvHNvP5PHZSJ7/ZkTm9ja1XvA1/9gxQbmTR3H4sNb+eUftoxgZDKWlCAkVvpzBf78vx7mwfU7+PK7TmDJnMljFouZcfnpR/Glty/ht8908M5v3s/N7RvZ1T3yY/kvdPVyWGvziL9uyazWA68gerN5VqzfyTkvn8U5Lz+Mhzfs5MWXMFwljUMJQmLD3fnELY9x16ptfO6CV3DBifWtYYjau145l2+852R27O3nE7c8zilfuJPr712L+8j1JrZ29TKrtWnEXm+oGROb2N2bO6Aq6JENnfTnCpyycBqnHzMdd3hg3Y4IopTRpgQhsfHv963np48+z5VnH8N7R2G20v449xWH8T9/9TqWX3Eapx09nc/fuooP/MdDvLDrwIdtSrr7c+zuzTFrUnQVxMyJxeSzrWv/P/k/8Ox2EgZt86fyitmTaEknWbFeCeJgoAQhsbBi/Q4+f+tTnHXcLK543dFjHU4oM2PJnMl865KT+eu3LObepzs49e/v5LLvrOCxjZ0H/LqlJBPlENPsyS0AbNzZvd/XPrBuB8cd3sqkljTpZIKTj5zCA88qQRwMlCCk4a3ZtocP/edDzJ06ji+/6wQSica+qY2ZcdlrFnDHlWfwoTOP4tGNnbz9G7/jew9sOKDX2xp8qp8VYYIorTRf17Fnv67L5Qs8urGTV87ft8HhsgVTWfVCVyS9GBldShDS0H635kXe9vX7cIdvXdIWyXbXUZk3bRwfP+dY7vrLM3ntoul86id/4Mu/Xr3f95VYv30vAHOnRLdqe1ZrE+MzSdZ27N2v657aspuebJ6Tj5wycGzZgqm4Q/tzqiLiTglCGtaPH97Epf/+ILNam/nph0/j6JkvfT+lsTCpJc23LmnjXW1z+Je71nDu1+7l+nvX8nxnT13XP711N83pBHOmtEQWo5mxcMYE1u5nBVFKAuUJ4sS5k8kkEzyoYabY00I5aTjuzr/etYYv3/40r144jW++7+RYVQ5hUskEX3z7Es44ZibX37uWz9+6is/fuoojJjXTlE7SlEpwysJpvP/U+cyfPn7QtQ9v6GTJ7MmRD60dd/hEfr1yK4WC1/2z2p/byRGTmjli8r7k1ZxOsmTOJPUhDgJKENJQnu/s4XO/WMl/P/ECFy6dzRffvoRMBPsPjQUz481LDufNSw5n/Yt7ue3JF1j1wm5yBWdXT5bvPbiB/7h/PeefcARXvP5ojp45kc2dPTy+qZOPviH6nWPb5k/l5vZNrO3Yw6JZE2ue7+60r9/BsgXD742xbMFUrr93Hd39uVHbAkVGnv7PyZjbtruXB5/dwU8e3sw9T3eQMLjqTcfywdMXRrb30FibP308HzzjqEHHtu3u5d9++yw33v8cP3vseY6fPYm9fTnSiQRvOyn6NR+vCu6kd9+aF+tKEE9t2c3Wrj5ee/T0Yc8tWzCVr9+zlkc2dHJayPMSD0oQMiaefXEv37hnDfet2c7mYCx+VmsTH3jtQt57yjzmRNiQbVQzJzbzqfOO48/OOIob73+O+9e9iJnxzxcfOyrbih85bTzHHjaRnz76PO8/rfZeSrev3ArAmcfOGPbcyUdOIWHw+3XblSBiTAlC9ssTm3fxm6c7mDGhifNPPOKA9kH6+WPP8/FbHsMwXn/cTN5/6nyWzpvM0nlTSDb4FC1aqJwAAAibSURBVNbRMHV8ho+ctYiPMPqbEL7j5Dn87S+fYsX6HYOmrg6VLzg3t2/k1KOmMXPi8Om3E5vTvHL+VH726PP8xVnHNPzUZAkX6eCumZ1rZqvNbI2ZXRXyfJOZ/SB4/gEzm1/23CeD46vN7Jwo45TauvtzXP2zJ3jLv/wP/3Dbaj7xo8d53T/ew12rttb9Gn25PH/z8yf5v99/hFccMYl7Pn4m1777JD5w+kLa5k9VcmgA737VPGa1NvFXtzzOtiqb9/1gxUY2d/ZUvf/Gu181jw07uvn5489HEaqMAhvJ/WIGvbBZEngaOBvYBKwALnb3lWXn/DmwxN3/zMwuAi509z82s8XA94FlwBHAHcAx7l5xo5i2tjZvb2+P5L/lULa3L8fdq7fxxV+tYuOOHv70tAV85A2LWLmli88uf5LVW3dz4dLZXPaaBSyaNYF0IkF/vkBnd5bOnn527s3S2d3Pyi1d/PjhzWzu7OH9p87nU+cdd9A0nw82Dz67g0tueIBxmRQXL5vLsgXTOKy1mfFNSbp6cvzPmg7+8ddPc9K8yXz/A6dU7BPlC86FX7+PZ1/cy2f+6OWcOHcSTakkTekETckkmVSCTCqhDwZjzMwecve20OciTBCvBj7r7ucEjz8J4O5fKDvntuCc+80sBbwAzACuKj+3/LxKP+9AE0Rndz/v+GbxZcv/Lgb9rXjotxXP90Hne/jxCn/tL+k1K5xPXecP/7nuxVtdusPCGeP5woXH86qF+2as9OXyXHvXGr5+z1pyNRZ/JRNG25FT+PDrjub0Y4aPWUtjeWbrbv7u1qf4zdMdob+rrzl6Ov/67qU1tyDf3NnDZd9ZwaoXdlc8J5kwMsnEQMJIJ+yAJidUuqTaSxnhT1a/ptLPqfBalV+q4pPVrgn7Occd3sq/XLy02k+q9noVE0SUPYjZwMayx5uAV1U6x91zZrYLmBYc//2Qa4dN4zCzy4HLAebNm3dAQSYTxsvKZ2xY6LeD/qcMPr5/5w9+/bJzKr5OHedX+AEv6TWDP6eMz3DykVM49ajpwz7pNaWSXPnGl3HJqfP57TMdbNzRQ8GdTCrBpJY0U8ZlmNySZvK4DHOmttDaHO+1DIeSRbMm8p0/WUZXb5aVz3fx4p4+9vblmNic5mWHTWTh9PF1vYnPntzCL//fa3l8UycbdnTTlyvQX/rK7/u+L5cfOJbNh3/YqPZZ1qn0iavaNRWOV/lBla/Z7x9f8edU/ahV4cm5ES2ijHWT2t2vB66HYgVxIK8xsTnNte85aUTjOtRMn9DEhUvnjHUYEoHW5jSnLBy+zmF/JBPG0nlTWDpvSu2TpaFEOQi8GZhb9nhOcCz0nGCIaRKwvc5rRUQkQlEmiBXAIjNbYGYZ4CJg+ZBzlgOXBt+/A7jLi3XXcuCiYJbTAmAR8GCEsYqIyBCRDTEFPYUrgNuAJHCDuz9pZtcA7e6+HPg2cKOZrQF2UEwiBOfdDKwEcsCHq81gEhGRkRfZLKbRpmmuIiL7r9osJk1EFxGRUEoQIiISSglCRERCKUGIiEiog6ZJbWYdwHNjHUeI6cCLYx3EflLM0YtbvKCYR8tox3yku4fuf3PQJIhGZWbtlWYINCrFHL24xQuKebQ0UswaYhIRkVBKECIiEkoJInrXj3UAB0AxRy9u8YJiHi0NE7N6ECIiEkoVhIiIhFKCEBGRUEoQETGzd5rZk2ZWMLO2Ic990szWmNlqMztnrGIcyszODWJaY2ZXjXU8YczsBjPbZmZPlB2bama3m9kzwZ8NdWcaM5trZneb2crgd+IjwfGGjdvMms3sQTN7LIj5b4LjC8zsgeB35AfBVv4Nw8ySZvaImf0ieNzo8a43sz+Y2aNm1h4ca5jfCyWI6DwBvA24t/ygmS2muK35y4Fzga+bWXL0wxssiOFa4E3AYuDiINZG8x2Kf2/lrgLudPdFwJ3B40aSAz7m7ouBU4APB3+3jRx3H/B6dz8BOBE418xOAb4IfNXdjwZ2ApeNYYxhPgI8Vfa40eMFeJ27n1i29qFhfi+UICLi7k+5++qQpy4AbnL3Pnd/FlgDLBvd6EItA9a4+zp37wduohhrQ3H3eyneO6TcBcB3g++/C7x1VIOqwd23uPvDwfe7Kb6BzaaB4/aiPcHDdPDlwOuBW4LjDRWzmc0B3gz8W/DYaOB4q2iY3wsliNE3G9hY9nhTcGysNWpc9Zjl7luC718AZo1lMNWY2XxgKfAADR53MFzzKLANuB1YC3S6ey44pdF+R74GfAIoBI+n0djxQjHp/trMHjKzy4NjDfN7Edkd5Q4FZnYHcFjIU59295+NdjxS/ORrZg05d9vMJgA/Aj7q7l3FD7hFjRh3cBfHE81sMvAT4NgxDqkiM3sLsM3dHzKzM8c6nv3wGnffbGYzgdvNbFX5k2P9e6EE8RK4+1kHcNlmYG7Z4znBsbHWqHHVY6uZHe7uW8zscIqfeBuKmaUpJof/cvcfB4cbPm4Ad+80s7uBVwOTzSwVfCpvpN+R04Dzzew8oBloBf6Jxo0XAHffHPy5zcx+QnGot2F+LzTENPqWAxeZWZOZLQAWAQ+OcUwAK4BFwayPDMVG+vIxjqley4FLg+8vBRqqegvGwr8NPOXuXyl7qmHjNrMZQeWAmbUAZ1PsndwNvCM4rWFidvdPuvscd59P8Xf3Lnd/Dw0aL4CZjTeziaXvgTdSnNzSOL8X7q6vCL6ACymOefYBW4Hbyp77NMXx3NXAm8Y61rK4zgOeDmL79FjHUyHG7wNbgGzw93sZxbHmO4FngDuAqWMd55CYX0NxrPlx4NHg67xGjhtYAjwSxPwEcHVwfCHFDzRrgB8CTWMda0jsZwK/aPR4g9geC76eLP2ba6TfC221ISIioTTEJCIioZQgREQklBKEiIiEUoIQEZFQShAiIhJKCUJEREIpQYiISKj/BR0tklBltQvnAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['subdomain_length'].plot(kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "CYhzfbOZudOR",
        "outputId": "6a97f3b0-cf83-430c-e039-a305f482398e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa143889790>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['subdomain_length'].plot.kde()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "-MnHR2tAuta_",
        "outputId": "ec08f314-da92-4f0a-93d4-57ddbcd470e7"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa14380f390>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for feature in data[['FQDN_count', 'subdomain_length', 'upper', 'lower',\n",
        "       'numeric', 'entropy', 'special', 'labels', 'labels_max',\n",
        "       'labels_average', 'len']].columns:\n",
        "  sns.boxplot(x='Target Attack',y=feature,data=data)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dWCe2PUEkVp5",
        "outputId": "2a2f1655-20a5-429e-b90b-e5dd9279827c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEGCAYAAACNaZVuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAXo0lEQVR4nO3df5RdZX3v8fcnE9BAsEKIKWsgDDC5urLEUpnrpZS6QgQbYQFC6o/cawlVb9oKMS1ooZpe4N5IpXrbFUeqYksJ/sCWAlfwxmD4EVxaQScYAyrKSAfKrABh8Af5cYFJvvePsw/MjJnJ7OTs85w5z+e11lnnPHvO2efDrKwPzzxnn70VEZiZWV6mpQ5gZmbN5/I3M8uQy9/MLEMufzOzDLn8zcwyND11gMk6/PDDo6urK3UMM7MpZePGjc9ExOyx26dM+Xd1ddHX15c6hpnZlCLpsT1t97KPmVmGXP5mZhly+ZuZZcjlb2aWIZd/hvr7+znzzDPp7+9PHcXMEnH5Z2jVqlVs376dVatWpY5iZom4/DPT39/PwMAAAAMDA579m2XK5Z+ZsbN9z/7N8uTyz0x91j/e2Mzy4PLPzNhTZPiUGWZ5cvlnZuXKlROOzSwPLv/MdHd3vzTb7+rqoru7O20gM0vC5Z+hlStXcvDBB3vWb5Yxl3+G1q5dy/bt27njjjtSRzGzRFz+GbrlllsAuOmmmxInMbNUXP6Z+dSnPjVqfM011yRKYmYpufwzU5/113n2b5Ynl7+ZWYZc/mZmGXL5Z+a8884bNX7HO96RKImZpeTyz8wHP/jBUeMLL7wwURIzS8nln6Genh4ATjrppMRJzCwVl3+GNm3aBEBfX1/iJGaWiss/M3fffTfDw8MADA8Pc8899yROZGYpuPwzc9VVV40af+xjH0uUxMxScvlnpj7rH29sZnlw+Wdm+vTpE47NLA8u/8x85CMfGTX+6Ec/miiJmaXk8s/MwoULX5rtT58+nVNPPTVxIjNLweWfofrs37N+s3y5/DPU29sLwKc//enEScxGW7BgwUs3q1al5S/pKEn3SPqRpB9KWlFsP0zSekmPFPeHVpnDRvv5z38OwNDQUOIkZpZK1TP/YeCSiJgPnARcKGk+cBlwV0TMA+4qxtYE55577qjx4sWLEyUxG23sbN+z/2pVWv4RsSUiHigePwf8GOgEzgHWFE9bA7y9yhz2svqsv86zf7M8NW3NX1IX8NvA/cCciNhS/OhJYM44r1kmqU9S39atW5uS08wsB00pf0kzgZuBP4uIX438WUQEEHt6XURcGxE9EdEze/bsJiQ1M8tD5eUv6QBqxf+liKhfQPYpSUcUPz8CeLrqHFZz6KGjP1ufNWtWoiRmllLVR/sI+EfgxxHxtyN+dBuwtHi8FPhqlTnsZbfeeuuo8c0335woidloGzZsmHBsjVX1zP93gT8EFkraVNzOAD4OnC7pEeC0YmxNUp/9e9Zvlq+qj/b5VkQoIt4QEScUt7URMRQRb4mIeRFxWkQ8W2UOG+0Tn/gEBx98MFdffXXqKGaj3HzzzbzhDW/wX6RN4G/4ZmjVqlVs376dVatWpY5iNsqaNWt48MEHueGGG1JHaXsu/8z09/czMDAAwMDAAP39/WkDmRWGhoZYt24dEcG6dev8HZSKufwzM3a279m/tYo1a9awe/duAHbt2uXZf8Vc/pmpz/rHG5ulcuedd466vvT69esTJ2pvLv/MdHV1TTg2S+W0004bda2J008/PXGi9ubyz8zKlSsnHJulsnTpUqZNq1VSR0cH559/fuJE7c3ln5nu7u6XZvtdXV10d3enDWRWmDVrFosWLUISixYt8vdQKubyz9COHTsAeP755xMnMRvt7LPP5qCDDuKss85KHaXtufwz9PTTtVMpbdmyZS/PNGuu2267jR07dnD77benjtL2XP6Zec973jNqfMEFF6QJYjaGj/NvLpd/Zp544olRYx/qaa3Cx/k3l8vfzFqCj/NvLpe/mbUEH+ffXC7/zBx55JGjxv6Sl7UKH+ffXC7/zHzxi18cNb7++uvTBDEbw8f5N5fLP0P12b9n/dZqli5dyvHHH+9ZfxOodv301tfT0xN9fX2pY5hZhRYsWPDSY1/GsTEkbYyInrHbPfM3M8uQy9/MWsLIWf+extZY01MHyElvb29LXDlrcHAQgM7OzqQ5uru7Wb58edIMZrly+Wdo586dqSOYWWIu/yZqlVnuihUrAFi9enXiJGaWitf8zcwy5PI3s5Yw9tBOH+pZrVLLPpJOBrpGvi4ifOo9M7MpZtLlL+kLwHHAJmBXsTkAl7+ZNYRn+81TZubfA8yPqfKVYDMzG1eZNf+HgN+sKoiZmTXPXmf+km6ntrxzCPAjSd8FXrryd0ScXV08MzOrwmSWfT5ZeQozM2uqvZZ/RNwLIOnqiLh05M8kXQ3cW1E2MzOrSJk1/z1dU+1tjQpiZmbNM5k1/z8FPgAcK2nziB8dAny7qmBmZladyaz5fxn4OvDXwGUjtj8XEc9WksrMsuSLuTTPXpd9IuKXETEAXAg8N+KGpAMmeq2k6yQ9LemhEduukDQoaVNxO2O//gvMzKy0Mmv+DwBbgZ8CjxSPByQ9IOnEcV5zPbBoD9v/LiJOKG5rywQ2s/bki7k0V5nyXw+cERGHR8Qsah/2fo3a5wF/v6cXRMQ3AS8NmZm1mDLlf1JE3FEfRMQ3gN+JiPuAV5R834skbS6WhQ4d70mSlknqk9S3devWkm9hZmbjKVP+WyRdKuno4vYXwFOSOoDdJfbzGWoniDsB2AL87/GeGBHXRkRPRPTMnj27xFuYmdlEypT/fwWOBP5PcZtbbOsA3jnZnUTEUxGxKyJ2A58H3lQig5mZNcCkz+oZEc8A412HcNJXJZd0RERsKYbnUjthnJllbsOGDT7Us4nKnM//PwEf4tcv5rJwgtfcCCwADpf0BHA5sEDSCdROFjcA/PE+5DYzs/1Q5nz+NwGfBf6Bly/mMqGIWLKHzf9Y4j3NLCOe7TdPmfIfjojPVJbEzMyapswHvrdL+oCkIyQdVr9VlszMzCpTZua/tLj/8IhtARzbuDhmlkJvby/9/ZM+bqMyg4ODAHR2dibN0d3dzfLl4x3f0h7KHO1zTJVBzMx27tyZOkI2yhztcxBwMTA3IpZJmge8NiK+Vlk6M2uKVpnlrlixAoDVq1cnTtL+yqz5/xPwAnByMR4EVjU8kZmZVa5M+R8XEX8DvAgQETsAVZLKzMwqVab8X5A0g9qHvEg6Dni+klRmZlapMkf7XA6sA46S9CXgd4ELqghlZmbVKnO0z3pJDwAnUVvuWVGc78fMzKaYyVzA/Y1jNtVPyjZX0tyIeKDxsczMrEqTmfmPe759auv/457YzczMWtNeyz8iTp3MjiSdHhHr9z+SmZlVrczRPntzdQP3ZWZmFWpk+fuYfzOzKaKR5R8N3JeZmVWokeVvZmZTRCPLf6CB+zIzswqV+YYvkk7m16/he0Nxf15Dk5mZWWXKnNL5C8BxwCZevoZvADdUkMvMzCpUZubfA8yPCH+wa2Y2xZVZ838I+M2qgpiZWfOUmfkfDvxI0ncZcSrniDi74anMzKxSZcr/iqpCmJlZc5U5pfO9VQYxM7Pmmcwpnb8VEadIeo7R3+IVEBHxqsrSmZlZJSZzVs9TivtDqo9jZmbNUOpLXgCSXgO8sj6OiMcbmsjMzCo36UM9JZ0t6RHg34F7qZ3O4esV5TIzswqVOc7/f1G7fu9PI+IY4C3AfZWkMjOzSpUp/xcjYgiYJmlaRNxD7Vu/ZmY2xZRZ8/+FpJnAN4EvSXoa2F5NLDMzq1KZmf85wE7gz4F1wM+As6oIZWZm1SrzJa/tAJJeBdxeWSIzM6tcmaN9/ljSk8BmoA/YWNxP9JrrJD0t6aER2w6TtF7SI8X9ofsa3szM9k2ZZZ8PAa+PiK6IODYijomIY/fymuuBRWO2XQbcFRHzgLuKsZmZNVGZD3x/Buwos/OI+KakrjGbzwEWFI/XABuAS8vs16yd9Pb20t/fnzpGS6j/HlasWJE4SWvo7u5m+fLlley7TPn/JfBvku5n9CmdP1jyPedExJbi8ZPAnPGeKGkZsAxg7ty5Jd/GbGro7+/nkR9+n7kzd+39yW3uwBdrixHPPzbhinIWHt/WUen+y5T/54C7gQeB3Y1484gISeNeGSwirgWuBejp6fEVxKxtzZ25i4+88VepY1gLueqBas+ZWab8D4iIixvwnk9JOiIitkg6Ani6Afs0M7MSynzg+3VJyyQdURyxc5ikw/bhPW8DlhaPlwJf3Yd9mJnZfigz819S3P/liG0BjHvEj6QbqX24e7ikJ4DLgY8D/yLpfcBjwDvLBDYzs/1X5ktex5TdeUQsGedHbym7LzMza5xJl7+kA4A/Bd5cbNoAfC4iXqwgl5mZVajMss9ngAOAvy/Gf1hse3+jQ5mZWbXKlP9/jojfGjG+W9IPGh3IzMyqV+Zon12SjqsPJB0L+FspZmZTUJmZ/4eBeyQ9Cgg4GvijSlKZmVmlyhztc5ekecBri00/iYjnJ3qNmZm1pr2Wv6TzxvlRtyQi4pYGZzIzs4pNZuZfv1rXa4CTqZ2GWcCpwL8BLn8zsylmr+UfEX8EIOkbwPz6GTmL8/JcX2k6MzOrRJmjfY4acSpmgKcAn2fZzGwKKnO0z12S7gBuLMbvAu5sfCSzvAwODrL9uY7KT+FrU8tjz3Vw8OBgZfsvc7TPRZLO5eXTO1wbEbdWE8vMzKpUZuYP8B1qF3LZDXyv8XHM8tPZ2cnzw1t8MRcb5aoHXsUrOjsr2/+k1/wlvR/4LnAu8AfAfZLeW1UwMzOrTtlv+P52RAwBSJpF7VDP66oIZmZm1SlztM8Q8NyI8XPFNjMzm2Im8w3f+nV7+4H7JX2V2hW8zgE2V5jNzMwqMplln0OK+58Vtzpfe9fMbIqazDd8r2xGEDMza54yl3G8h9pyzygRsbChiSrS29tLf39/6hgtof57WLFiReIkraG7u5vly5cnzfD4Nn/JC+CpHbWPIecctDtxkvQe39bBvAr3X+Zonw+NePxKYDEw3Ng41env72fTQz9m10GHpY6S3LQXav8P3/joU4mTpNex49nUEeju7k4doWW8UExMXnG0fyfzqPbfRplv+G4cs+nbkr7b4DyV2nXQYex83RmpY1gLmfHw2tQRkv/V0Urqf42uXr06cZL2V2bZZ+SUeRrQA/xGwxOZmVnlyiz7bKS25i/gRWAAeF8FmczMrGJlvuR1KXBCRBwDfAHYDuyoJJWZmVWqTPmvjIhfSToFWAj8A/CZamKZmVmVypT/ruL+TODzEfF/gQMbH8nMzKpWpvwHJX2O2kVc1kp6RcnXm5lZiyhT3u8E7gB+PyJ+ARxG7UyfZmY2xZQ5zn8HcMuI8RZgy/ivMDOzVuVlGzOzDLn8zcwy5PI3M8uQy9/MLENlTu/QUJIGqF0KchcwHBE9qbKYmeUmWfkXTo2IZxJnMDPLjpd9zMwylLL8A/iGpI2Slu3pCZKWSeqT1Ld169YmxzMza18py/+UiHgj8DbgQklvHvuEiLg2Inoiomf27NnNT2hm1qaSlX9EDBb3TwO3Am9KlcXMLDdJyl/SwZIOqT8G3go8lCKLmVmOUh3tMwe4VVI9w5cjYl2iLGZm2UlS/hHxKPBbKd7bzMx8qKeZWZZc/mZmGXL5m5llyOVvZpYhl7+ZWYZc/mZmGXL5m5llyOVvZpYhl7+ZWYZc/mZmGUp9Ja+mGRwcpGPHL5nx8NrUUayFdOwYYnBwOHWM5Hp7e+nv708d46UMK1asSJqju7ub5cuXJ81QtWzK38xa34wZM1JHyEY25d/Z2cmTz09n5+vOSB3FWsiMh9fS2TkndYzk2n2Wa7/Oa/5mZhly+ZuZZcjlb2aWIZe/mbWMvr4+Fi5cyMaNG1NHaXsufzNrGVdccQW7d+/m8ssvTx2l7bn8zawl9PX1sW3bNgC2bdvm2X/FXP5m1hKuuOKKUWPP/qvl8jezllCf9Y83tsZy+ZtZS5g5c+aEY2ssl7+ZtYSxyz5XXnllmiCZcPmbWUvo6el5abY/c+ZMTjzxxMSJ2pvL38xaxsUXXwzAJZdckjhJ+3P5m1nL2LRpE5LYtGlT6ihtz+VvZi1haGiIdevWERGsW7eOoaGh1JHamsvfzFrCmjVr2L17NwC7du3ihhtuSJyovbn8zawl3HnnnQwP166qNjw8zPr16xMnam8ufzNrCaeddhrTp9euLzV9+nROP/30xInam8vfzFrC0qVLmTatVkkdHR2cf/75iRO1N5e/mbWEWbNmsWjRIiSxaNEiZs2alTpSW8vmGr5m1vqWLl3KwMCAZ/1N4Jm/mbWMxYsXs3nzZhYvXpw6SttLVv6SFkn6iaR+SZelymFmlqMk5S+pA7gGeBswH1giaX6KLGbWGhYsWDDh2Bor1Zr/m4D+iHgUQNJXgHOAH1X5ph07nmXGw2urfIsJTft/v0K7X0z2/q0mph3A7le+KmmGjh3PAnOSZjBLIVX5dwL/MWL8BPBfxj5J0jJgGcDcuXP36w27u7v36/WNMDg4zM6dO1PHaBkzZsygszN18c5piX8bZs3W0kf7RMS1wLUAPT09sT/7Wr58eUMymZm1g1Qf+A4CR40YH1lsMzOzJkhV/t8D5kk6RtKBwLuB2xJlMbMWsGHDhgnH1lhJln0iYljSRcAdQAdwXUT8MEUWM7McJVvzj4i1QLpDb8ys5Xi23zz+hq+ZWYZc/mZmGXL5m5llyOVvZpYhRezXd6eaRtJW4LHUOdrI4cAzqUOY7YH/bTbW0RExe+zGKVP+1liS+iKiJ3UOs7H8b7M5vOxjZpYhl7+ZWYZc/vm6NnUAs3H432YTeM3fzCxDnvmbmWXI5W9mliGXf2YkLZL0E0n9ki5LncesTtJ1kp6W9FDqLDlw+WdEUgdwDfA2YD6wRNL8tKnMXnI9sCh1iFy4/PPyJqA/Ih6NiBeArwDnJM5kBkBEfBN4NnWOXLj889IJ/MeI8RPFNjPLjMvfzCxDLv+8DAJHjRgfWWwzs8y4/PPyPWCepGMkHQi8G7gtcSYzS8Dln5GIGAYuAu4Afgz8S0T8MG0qsxpJNwLfAV4r6QlJ70udqZ359A5mZhnyzN/MLEMufzOzDLn8zcwy5PI3M8uQy9/MLEMuf5vSJM2StKm4PSlpcMT4wAa/16slfWAvz3m7pJD0uhHbTpB0xojxAkkn70eObfv6WrM6l79NaRExFBEnRMQJwGeBv6uPi5PX7ZGk6fvwdq8GJix/YAnwreK+7gTgjBHjBcA+l79ZI7j8re1I+u+SvifpB5JulnRQsf16SZ+VdD/wN5KOk3SfpAclrRo5o5b04WIfmyVdWWz+OHBc8VfFJ/bwvjOBU4D3Ufv2NMVfH/8TeFfxukuBPwH+vBj/nqSzJN0v6fuS7pQ0p74/Sf9U5NssafGY9ztc0nckndno36G1v32Z/Zi1ulsi4vMAklZRK+Pe4mdHAidHxC5JXwNWR8SNkv6k/mJJbwXmUTsFtoDbJL0ZuAx4ffFXxp6cA6yLiJ9KGpJ0YkRslPQ/gJ6IuKjY/wxgW0R8shgfCpwUESHp/cBfAJcAfwX8MiKOH/G8esY51E7NsTIi1u/vL8zy4/K3dvT6ovRfDcykdjqLupsiYlfx+HeAtxePvwx8snj81uL2/WI8k9r/DB7fy/suAVYXj79SjDdOIu+RwD9LOgI4EPj3YvtpFH9BAETEz4uHBwB3ARdGxL2T2L/Zr3H5Wzu6Hnh7RPxA0gXU1tjrtk/i9QL+OiI+N2qj1DXuC6TDgIXA8ZIC6ABC0ocn8X69wN9GxG2SFgBX7OX5w9T+p/L7gMvf9onX/K0dHQJskXQA8N8meN59QH0d/d0jtt8BvLdYw0dSp6TXAM8V+96TPwC+EBFHR0RXRBxFbQb/e3t43djxb/DyqbWXjti+HriwPhix7BPAe4HXFZ8hmJXm8rd29FfA/cC3gYcneN6fARdL2gx0A78EiIhvUFsG+o6kB4F/BQ6JiCHg25Ie2sMHvkuAW8dsu7nYfg8wv/iA913A7cC59Q98qc30b5K0EXhmxOtXAYcW7/cD4NT6D4qlqyXAwr0dfmq2Jz6rp2WrOApoZ/FB67uBJRHhaxpbFrzmbzk7Efi0JAG/oLaUYpYFz/zNzDLkNX8zswy5/M3MMuTyNzPLkMvfzCxDLn8zswz9f1SfcpUjob0oAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Preprocessing and EDA After it "
      ],
      "metadata": {
        "id": "W6VvombAvwgN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1-DropNA\n"
      ],
      "metadata": {
        "id": "ggd-t4t_whZ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = data.replace((np.inf, -np.inf, np.nan), 0).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "sJy9bFACwGC_"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_2mizyAwSyu",
        "outputId": "511ed861-65ec-4d3c-e565-96a4c31924c6"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "timestamp           0\n",
              "FQDN_count          0\n",
              "subdomain_length    0\n",
              "upper               0\n",
              "lower               0\n",
              "numeric             0\n",
              "entropy             0\n",
              "special             0\n",
              "labels              0\n",
              "labels_max          0\n",
              "labels_average      0\n",
              "longest_word        0\n",
              "sld                 0\n",
              "len                 0\n",
              "subdomain           0\n",
              "Target Attack       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2-Remove Time Stamp and got target in var y"
      ],
      "metadata": {
        "id": "SrHG6IcevD47"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=new_data.drop(labels = [\"Target Attack\", \"timestamp\"], axis=1) \n",
        "y=new_data[\"Target Attack\"]"
      ],
      "metadata": {
        "id": "AhiC4X_kwFUZ"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3-Hash encoder of categorical columns"
      ],
      "metadata": {
        "id": "8lv0GfOpxvg6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## encode the two categorical data \n",
        "hash = HashingEncoder()\n",
        "encoded_data = hash.fit_transform(X)\n"
      ],
      "metadata": {
        "id": "89tFFPiw1-uz"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##jde plot that show the density of ech columns\n",
        "kde_hist_plot(encoded_data,row=10,col=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9xKIMjMMmjzt",
        "outputId": "2c2ec811-7aef-4efe-f6a0-0fd2b98729aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x3600 with 40 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "zhwPzPGgpVLz"
      },
      "outputs": [],
      "source": [
        "y=pd.DataFrame(y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y['Target Attack'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-2aQDbkmGsb",
        "outputId": "0b31c9ec-0347-4536-9f69-b7455b8d66bc"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    147179\n",
              "0    120895\n",
              "Name: Target Attack, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#using seaborns countplot to show classes \n",
        "import seaborn as sns\n",
        "fig, ax = plt.subplots()\n",
        "g = sns.countplot(y['Target Attack'], palette='viridis')\n",
        "g.set_xticklabels(['not malicious url DNS', 'malicious DNS'])\n",
        "g.set_yticklabels([])\n",
        "\n",
        "# function to show values on bars\n",
        "def show_values_on_bars(axs):\n",
        "    def _show_on_single_plot(ax):        \n",
        "        for p in ax.patches:\n",
        "            _x = p.get_x() + p.get_width() / 2\n",
        "            _y = p.get_y() + p.get_height()\n",
        "            value = '{:.0f}'.format(p.get_height())\n",
        "            ax.text(_x, _y, value, ha=\"center\") \n",
        "\n",
        "    if isinstance(axs, np.ndarray):\n",
        "        for idx, ax in np.ndenumerate(axs):\n",
        "            _show_on_single_plot(ax)\n",
        "    else:\n",
        "        _show_on_single_plot(axs)\n",
        "show_values_on_bars(ax)\n",
        "\n",
        "sns.despine(left=True, bottom=True)\n",
        "plt.xlabel('')\n",
        "plt.ylabel('')\n",
        "plt.title('Distribution of Transactions', fontsize=30)\n",
        "plt.tick_params(axis='x', which='major', labelsize=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Qgdl3c7IsRW8",
        "outputId": "b13d365c-e490-4888-c770-36b32167ed50"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 4-Split the dataset "
      ],
      "metadata": {
        "id": "gPc-9sRYeiny"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(encoded_data,y, test_size=0.20, random_state=42,stratify=y)\n",
        "print(\"train data \" ,X_train.shape)\n",
        "print(\"train label \" ,y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvfHDGZ6tzxt",
        "outputId": "33a287a1-1634-4589-fcf5-90a7f898a5f3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train data  (214459, 20)\n",
            "train label  (214459, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 5-Roubst scaller sense to outliers"
      ],
      "metadata": {
        "id": "FBK3G_tDezkC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roubst=RobustScaler()\n",
        "x_tr_scaled=roubst.fit_transform(X_train)\n",
        "x_tst_scaled=roubst.transform(X_test)"
      ],
      "metadata": {
        "id": "3PTxo_dOtzuH"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Base line Modeles\n"
      ],
      "metadata": {
        "id": "e9trN17ZfC5O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1- Xgboost "
      ],
      "metadata": {
        "id": "vXm5Uhl8fGoY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Model1_xgboost=XGBClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "W06DQxMqfF2l"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time \n",
        "Model1_xgboost_pred=baseline_model(Model1_xgboost,x_tr_scaled,y_train,x_tst_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cN5RuH_rfSXd",
        "outputId": "ae0583af-3013-43ed-cf72-5374c8bbb68d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 12.9 s, sys: 5.54 ms, total: 12.9 s\n",
            "Wall time: 12.8 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_information(Model1_xgboost_pred,y_test,\"xgboost baseline\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "PSPZER_qfSUD",
        "outputId": "5eb81deb-363f-4e04-88f6-719133909905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall score : \t 0.9993545318657426\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.61      0.76     24179\n",
            "           1       0.76      1.00      0.86     29436\n",
            "\n",
            "    accuracy                           0.83     53615\n",
            "   macro avg       0.88      0.81      0.81     53615\n",
            "weighted avg       0.87      0.83      0.82     53615\n",
            "\n",
            "[[14857  9322]\n",
            " [   19 29417]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####2-random Forest"
      ],
      "metadata": {
        "id": "FMrcr-jmhPk-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Mode4_RFC = RandomForestClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "9u9AGG_DfSRq"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "Mode4_RFC_pred=baseline_model(Mode4_RFC,x_tr_scaled,y_train,x_tst_scaled)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWHa2PnJfSPe",
        "outputId": "0b4f6ffc-93a1-4b40-d20b-8a8427ffe3f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 10 s, sys: 0 ns, total: 10 s\n",
            "Wall time: 10 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_information(Mode4_RFC_pred,y_test,\"Model RandomForestClassifier baseline\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "h2GyGtldhc45",
        "outputId": "bd5cf57e-72b7-4dcf-80f8-9fcffe551969"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall score : \t 0.9995583639081397\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.61      0.76     24179\n",
            "           1       0.76      1.00      0.86     29436\n",
            "\n",
            "    accuracy                           0.83     53615\n",
            "   macro avg       0.88      0.81      0.81     53615\n",
            "weighted avg       0.87      0.83      0.82     53615\n",
            "\n",
            "[[14856  9323]\n",
            " [   13 29423]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### feature selection Annova mutual  with xgboost"
      ],
      "metadata": {
        "id": "tVTcEF9mCqIf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### 1) xgboost with Annova"
      ],
      "metadata": {
        "id": "ZI4w21zFC_Pb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f1_acc, feature=feature_selection(x_tr_scaled, y_train, x_tst_scaled, y_test,Model1_xgboost, 20, True)"
      ],
      "metadata": {
        "id": "X8phkSFoUIMs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12cc6a05-b8ec-4880-e845-2c9a8fbc09a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature 1 finished\n",
            "feature 2 finished\n",
            "feature 3 finished\n",
            "feature 4 finished\n",
            "feature 5 finished\n",
            "feature 6 finished\n",
            "feature 7 finished\n",
            "feature 8 finished\n",
            "feature 9 finished\n",
            "feature 10 finished\n",
            "feature 11 finished\n",
            "feature 12 finished\n",
            "feature 13 finished\n",
            "feature 14 finished\n",
            "feature 15 finished\n",
            "feature 16 finished\n",
            "feature 17 finished\n",
            "feature 18 finished\n",
            "feature 19 finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(feature, f1_acc, label = \"ANOVA Features test F1 score accuracy\")\n",
        "plt.title(\"ANOVA number of features-test F1 score accuracy\")\n",
        "plt.xlabel(\"number of features\")\n",
        "plt.ylabel(\"test F1 score accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "56Z3zr_KVQKt",
        "outputId": "33fb54e0-41ec-493e-fd4c-4fd98a9b3142",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(f1_acc)"
      ],
      "metadata": {
        "id": "kBTFD0mhaK_f",
        "outputId": "32fa69d7-a52f-4dbf-ccbd-74e9583b6ba5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8629976237275209"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of feature based on Annova:\\t\",feature[np.argmax(np.array(f1_acc))])"
      ],
      "metadata": {
        "id": "ZUdcbnr1grTR",
        "outputId": "70539d81-9453-42db-b0a6-a5094631dffb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of feature based on Annova:\t 15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_data.columns[selecter.get_support()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fCsywWnvQ-2",
        "outputId": "f012e529-f7c9-4aa8-cf0c-6c02f63f26e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['col_0', 'col_1', 'col_4', 'col_5', 'col_6', 'col_7', 'FQDN_count',\n",
              "       'subdomain_length', 'lower', 'numeric', 'special', 'labels', 'len',\n",
              "       'subdomain'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### 2) xgboost with MUTUAL INFO"
      ],
      "metadata": {
        "id": "DGfnt9xPJT1A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f1_acc, feature=feature_selection(x_tr_scaled, y_train, x_tst_scaled, y_test,Model1_xgboost, 20, False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4BHZnrkJTZG",
        "outputId": "ac819cc8-2bf1-4578-ca5c-eab035e067ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature 1 finished\n",
            "feature 2 finished\n",
            "feature 3 finished\n",
            "feature 4 finished\n",
            "feature 5 finished\n",
            "feature 6 finished\n",
            "feature 7 finished\n",
            "feature 8 finished\n",
            "feature 9 finished\n",
            "feature 10 finished\n",
            "feature 11 finished\n",
            "feature 12 finished\n",
            "feature 13 finished\n",
            "feature 14 finished\n",
            "feature 15 finished\n",
            "feature 16 finished\n",
            "feature 17 finished\n",
            "feature 18 finished\n",
            "feature 19 finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(feature, f1_acc, label = \"Mutual Features test F1 score accuracy\")\n",
        "plt.title(\"Mutual number of features-test F1 score accuracy\")\n",
        "plt.xlabel(\"number of features\")\n",
        "plt.ylabel(\"test F1 score accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "G8Kq2EqkC6I_",
        "outputId": "bdae58c6-3bd1-4b35-c00b-2084f86d2d28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(f1_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bshuhonZC6Fh",
        "outputId": "b27f28aa-ac81-4889-c128-c7962313389f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8629849651631829"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of feature based on Mutual:\\t\",feature[np.argmax(np.array(f1_acc))])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KIER_o8C6Ds",
        "outputId": "421a198e-a070-45be-f1de-da3e95f1aa5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of feature based on Mutual:\t 17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### feature selection Annova mutual  with Random Forest\n"
      ],
      "metadata": {
        "id": "ICGISL0NOCNU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### 1) RandomForest with Annova"
      ],
      "metadata": {
        "id": "Wia_Vj07N5aA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Mode4_RFC = RandomForestClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "v_RIVLNZp4Q4"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_acc, feature=feature_selection(x_tr_scaled, y_train, x_tst_scaled, y_test,Mode4_RFC, 20, True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXZPiswBN5aF",
        "outputId": "8cc91050-696e-477a-8179-2e3129b087f0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature 1 finished\n",
            "feature 2 finished\n",
            "feature 3 finished\n",
            "feature 4 finished\n",
            "feature 5 finished\n",
            "feature 6 finished\n",
            "feature 7 finished\n",
            "feature 8 finished\n",
            "feature 9 finished\n",
            "feature 10 finished\n",
            "feature 11 finished\n",
            "feature 12 finished\n",
            "feature 13 finished\n",
            "feature 14 finished\n",
            "feature 15 finished\n",
            "feature 16 finished\n",
            "feature 17 finished\n",
            "feature 18 finished\n",
            "feature 19 finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(feature, f1_acc, label = \"ANOVA Features test F1 score accuracy\")\n",
        "plt.title(\"ANOVA number of features-test F1 score accuracy\")\n",
        "plt.xlabel(\"number of features\")\n",
        "plt.ylabel(\"test F1 score accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "outputId": "1279f1d6-3122-4075-ce02-39c08bcf4ae7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "PJMVSQbnN5aF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(f1_acc)"
      ],
      "metadata": {
        "outputId": "d941f368-09d0-40d0-830d-1aa9c64aff9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTjTuFr2N5aF"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8631683923386032"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of feature based on Annova:\\t\",feature[np.argmax(np.array(f1_acc))])"
      ],
      "metadata": {
        "outputId": "fffdeb14-eb89-4951-8045-c85b0d43a554",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRmWtglDN5aF"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of feature based on Annova:\t 14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### 2) Random Forest with MUTUAL INFO"
      ],
      "metadata": {
        "id": "7xChWqGtN5aF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f1_acc, feature=feature_selection(x_tr_scaled, y_train, x_tst_scaled, y_test,Mode4_RFC, 20, False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4990e7d2-635b-4d97-fd74-a610980aad88",
        "id": "-f8Y7PkyN5aF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature 1 finished\n",
            "feature 2 finished\n",
            "feature 3 finished\n",
            "feature 4 finished\n",
            "feature 5 finished\n",
            "feature 6 finished\n",
            "feature 7 finished\n",
            "feature 8 finished\n",
            "feature 9 finished\n",
            "feature 10 finished\n",
            "feature 11 finished\n",
            "feature 12 finished\n",
            "feature 13 finished\n",
            "feature 14 finished\n",
            "feature 15 finished\n",
            "feature 16 finished\n",
            "feature 17 finished\n",
            "feature 18 finished\n",
            "feature 19 finished\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(feature, f1_acc, label = \"Mutual Features test F1 score accuracy\")\n",
        "plt.title(\"Mutual number of features-test F1 score accuracy\")\n",
        "plt.xlabel(\"number of features\")\n",
        "plt.ylabel(\"test F1 score accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "3d472665-48ab-4170-ff27-98531968f8ff",
        "id": "gCi9Ach_N5aG"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(f1_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14fcf033-c3d3-4b97-99ff-a40ac521511b",
        "id": "3AsVmN_LN5aG"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8630723651403596"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"number of feature based on Mutual:\\t\",feature[np.argmax(np.array(f1_acc))])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c473406-e0b5-430a-9c3a-1e3a80ab75bc",
        "id": "wvQxDN_EN5aG"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of feature based on Mutual:\t 16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tunned RandomForest\n"
      ],
      "metadata": {
        "id": "WR5XnPHDcnUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Mode4_RFC = RandomForestClassifier(random_state=42)\n",
        "selecter = SelectKBest(f_classif, k = 14)\n",
        "X_new_annova_rf = selecter.fit_transform(x_tr_scaled, y_train)\n",
        "X_test_new_annova_rf = selecter.transform(x_tst_scaled)\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(Mode4_RFC, X_new_annova_rf, y_train, cv=10,scoring='f1')\n",
        "print(\"f1score: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPATgYjGltuO",
        "outputId": "5c199621-c60f-40a9-d6a0-9a5fa48b1697"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "f1score: 0.86 (+/- 0.00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import make_scorer\n",
        "rfc = RandomForestClassifier( random_state=42)\n",
        "\n",
        "param_dist = {\n",
        "    'max_depth': [2, 4, 8, 12],\n",
        "    'min_samples_split': [2, 4, 6, 8],\n",
        "    'n_estimators':[10,100,200,250]\n",
        "}\n",
        "\n",
        "scorer = make_scorer(f1_score)\n",
        "\n",
        "\n",
        "gs = GridSearchCV(estimator=rfc, param_grid=param_dist, n_jobs = -1 ,\n",
        "                        cv=5, scoring=scorer)\n",
        "\n",
        "\n",
        "gs.fit(X_new_annova_rf, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbQ97MG_c-zb",
        "outputId": "ef3d6269-758f-42f5-ebb7-882c768a20b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:926: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  self.best_estimator_.fit(X, y, **fit_params)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42), n_jobs=-1,\n",
              "             param_grid={'max_depth': [2, 4, 8, 12],\n",
              "                         'min_samples_split': [2, 4, 6, 8],\n",
              "                         'n_estimators': [10, 100, 200, 250]},\n",
              "             scoring=make_scorer(f1_score))"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gs.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OEXkcYmhdhJl",
        "outputId": "fd62fc7a-deab-4219-dbc9-3e57f6981d8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 12, 'min_samples_split': 4, 'n_estimators': 200}"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##tunned random forest\n",
        "rfc_tunned = RandomForestClassifier( random_state=42,max_depth=12, min_samples_split= 4, n_estimators= 200)\n"
      ],
      "metadata": {
        "id": "KMBJarlzc0eA"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "scores = cross_val_score(rfc_tunned, X_new_annova_rf, y_train, cv=10,scoring='f1')\n",
        "print(\"F1 score : %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()))  \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p27W_VVBc0Ru",
        "outputId": "d1b75ba4-bfbd-49a3-b5db-c7d511808c30"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 score : 0.86 (+/- 0.00)\n",
            "CPU times: user 2min 33s, sys: 1.29 s, total: 2min 35s\n",
            "Wall time: 2min 34s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Tunned XGBOOST\n"
      ],
      "metadata": {
        "id": "Lzl2xyqM7f2N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### randomForest with best  15 feature from Annova to see the f1 score accuracy\n",
        "selecter_xgb = SelectKBest(f_classif, k = 15)\n",
        "X_new_annova_xgb = selecter_xgb.fit_transform(x_tr_scaled, y_train)\n",
        "X_test_new_annova_xgb = selecter_xgb.transform(x_tst_scaled)\n",
        "\n",
        "scores_xgb = cross_val_score(Model1_xgboost, X_new_annova_xgb, y_train, cv=10,scoring='f1')\n",
        "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_xgb.mean(), scores_xgb.std()))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "737a3e76-d447-4d34-9e58-2e82e45e7116",
        "id": "bOHZ35n07f2N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.86 (+/- 0.00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import make_scorer\n",
        "xgbb=XGBClassifier(random_state=42)\n",
        "\n",
        "param_dist = {\n",
        "    'eta': [.3,.6],\n",
        "    'max_depth': [15, 20],\n",
        "    'min_child_weight':[6,8],\n",
        "    'subsample':[.7,.9],\n",
        "    'colsample_bytree':[.7,.9],\n",
        "\n",
        "}\n",
        "\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "# Build a random search using param_dist, rfr, and scorer\n",
        "random_search = RandomizedSearchCV(estimator=xgbb,\n",
        "                                   param_distributions=param_dist,\n",
        "                                   n_iter=10,\n",
        "                                   cv=5,\n",
        "                                   scoring=f1_score\n",
        "                                  )\n",
        "\n",
        "\n",
        "random_search.fit(X_new_annova_xgb, y_train)"
      ],
      "metadata": {
        "id": "tNVyOMUZ7f2N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e4cd1d2-0488-42a1-96fc-23f80538e858",
        "id": "cHYgw-w97f2N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'subsample': 0.9,\n",
              " 'min_child_weight': 8,\n",
              " 'max_depth': 20,\n",
              " 'eta': 0.6,\n",
              " 'colsample_bytree': 0.9}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "###Tunning the xgboost with best parameter \n",
        "xgboost_tunned = XGBClassifier(random_state=42,subsample= 0.9,\n",
        " min_child_weight=8,\n",
        " max_depth=20,\n",
        " eta= 0.6,colsample_bytree=0.9,\n",
        " n_estimators=200,\n",
        " )\n"
      ],
      "metadata": {
        "id": "zajMH4m67f2N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "scores_xgb_tunned = cross_val_score(xgboost_tunned, X_new_annova_xgb, y_train, cv=10,scoring='f1')\n",
        "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_xgb_tunned.mean(), scores_xgb_tunned.std()))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3256a266-c2e2-4568-8a63-d2b66d441ba3",
        "id": "QT3uDM3-7f2O"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.86 (+/- 0.00)\n",
            "CPU times: user 10min 18s, sys: 1.11 s, total: 10min 20s\n",
            "Wall time: 10min 18s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### ROC Curve\n"
      ],
      "metadata": {
        "id": "R2yH-oafIb60"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##roc_curve for tunned xgboost with best number of feature \n",
        "# Import necessary modules\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "# Compute predicted probabilities: y_pred_prob\n",
        "xgboost_tunned.fit(X_new_annova_xgb,y_train)\n",
        "y_pred_prob = xgboost_tunned.predict_proba(X_test_new_annova_xgb)[:,1]\n",
        "\n",
        "# Generate ROC curve values: fpr, tpr, thresholds\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr, tpr)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve for tunned xgboost with best number of feature ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Z4vFZNFmL9xY",
        "outputId": "0bfe71fc-e1f1-43c0-c1fa-213f0b0270be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##roc_curve for champion model tunned random Forest classifier with best feature \n",
        "# Import necessary modules\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "# Compute predicted probabilities: y_pred_prob\n",
        "rfc_tunned.fit(X_new_annova_rf,y_train)\n",
        "y_pred_prob = rfc_tunned.predict_proba(X_test_new_annova_rf)[:,1]\n",
        "\n",
        "# Generate ROC curve values: fpr, tpr, thresholds\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr, tpr)\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve for tunned random gorest with best number of feature <Champion model>')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "4cdPmgMoKlAk",
        "outputId": "d3eb5f3c-ab1f-4692-f53c-810190218c84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The champion model is randomforest beacause it has less number of feature compared to xgboost and it takes less tim than xgboost  "
      ],
      "metadata": {
        "id": "wrEmgW51F57Q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### pickles for Champion model random forest tunned  With all data and best number of feature from annova "
      ],
      "metadata": {
        "id": "WjszzT5hdMae"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "##save hash encoder as pickle file  that i will use it in dynamic model \n",
        "pickle.dump(hash, open('hashed_pickl_a.pkl', 'wb'))"
      ],
      "metadata": {
        "id": "Cs560DJqqjNM"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_tunned_ = RandomForestClassifier( random_state=42,max_depth=12, min_samples_split= 4, n_estimators= 200)\n",
        "selecter2 = SelectKBest(f_classif, k = 14)\n",
        "X_new_annova_rf_ = selecter2.fit_transform(x_tr_scaled, y_train)\n",
        "X_test_new_annova_rf_ = selecter2.transform(x_tst_scaled)\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(rfc_tunned_, X_new_annova_rf_, y_train, cv=10,scoring='f1')\n",
        "print(\"f1score: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJV_-T2KrbEV",
        "outputId": "354ae3d2-6504-4810-bab5-b93c5bbab92a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "f1score: 0.86 (+/- 0.00)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_data.columns[selecter2.get_support()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pe1fs2UpsF7U",
        "outputId": "1be3aef1-09e5-493c-9495-3be4f66762f7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['col_0', 'col_1', 'col_4', 'col_5', 'col_6', 'col_7', 'FQDN_count',\n",
              "       'subdomain_length', 'lower', 'numeric', 'special', 'labels', 'len',\n",
              "       'subdomain'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "updated_data=encoded_data[['col_0', 'col_1', 'col_4', 'col_5', 'col_6', 'col_7', 'FQDN_count',\n",
        "       'subdomain_length', 'lower', 'numeric', 'special', 'labels', 'len',\n",
        "       'subdomain']]"
      ],
      "metadata": {
        "id": "NTCUcj7csIrc"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roubst_all=RobustScaler()\n",
        "robst_all_data=roubst_all.fit_transform(updated_data)"
      ],
      "metadata": {
        "id": "Cjjy6KoJsLKu"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##imo\n",
        "pickle.dump(roubst_all, open('roubst_scaller_alldata_ver2.pkl', 'wb'))\n"
      ],
      "metadata": {
        "id": "gTAp79RysOIu"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rfc_tunned_.fit(robst_all_data,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Gd-ECBcsU63",
        "outputId": "46350855-3a73-4ba2-9123-065bd63b6fd5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=12, min_samples_split=4, n_estimators=200,\n",
              "                       random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(rfc_tunned_, open('Final_rf_model2.pkl', 'wb'))"
      ],
      "metadata": {
        "id": "umE1eP0NsbxI"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_rf=rfc_tunned_.predict(X_test_new_annova_rf_)"
      ],
      "metadata": {
        "id": "xFJo4a8Ut60Y"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"F1 Score Accuracy\\t\",f1_score(y_test, prediction_rf))\n",
        "print(\"Recall Score Accuracy\\t\",recall_score(y_test, prediction_rf))\n",
        "\n",
        "print(\"\\n\")\n",
        "print(classification_report(y_test, prediction_rf))    \n",
        "cm = confusion_matrix(y_test, prediction_rf, labels=[0,1])\n",
        "print(cm)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=[0,1])\n",
        "disp.plot()\n",
        "plt.title(\"Confusion of the champion model random forest tunned \")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "Wv67uPkxuIwO",
        "outputId": "999e92de-0378-4cbd-c5d7-f63f0ab072fe"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 Score Accuracy\t 0.8631477054397724\n",
            "Recall Score Accuracy\t 0.9996602799293383\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.61      0.76     24179\n",
            "           1       0.76      1.00      0.86     29436\n",
            "\n",
            "    accuracy                           0.83     53615\n",
            "   macro avg       0.88      0.81      0.81     53615\n",
            "weighted avg       0.87      0.83      0.82     53615\n",
            "\n",
            "[[14858  9321]\n",
            " [   10 29426]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lCIYHy1HvAlj"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "4MxEiJt-ipT2"
      ],
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}