UBC-datatset-expriments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Querylength</th>\n",
       "      <th>domain_token_count</th>\n",
       "      <th>path_token_count</th>\n",
       "      <th>avgdomaintokenlen</th>\n",
       "      <th>longdomaintokenlen</th>\n",
       "      <th>avgpathtokenlen</th>\n",
       "      <th>tld</th>\n",
       "      <th>charcompvowels</th>\n",
       "      <th>charcompace</th>\n",
       "      <th>ldl_url</th>\n",
       "      <th>...</th>\n",
       "      <th>SymbolCount_FileName</th>\n",
       "      <th>SymbolCount_Extension</th>\n",
       "      <th>SymbolCount_Afterpath</th>\n",
       "      <th>Entropy_URL</th>\n",
       "      <th>Entropy_Domain</th>\n",
       "      <th>Entropy_DirectoryName</th>\n",
       "      <th>Entropy_Filename</th>\n",
       "      <th>Entropy_Extension</th>\n",
       "      <th>Entropy_Afterpath</th>\n",
       "      <th>URL_Type_obf_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>23</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.690780</td>\n",
       "      <td>0.750744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.742480</td>\n",
       "      <td>0.751096</td>\n",
       "      <td>0.742274</td>\n",
       "      <td>Defacement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14306</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>4.333334</td>\n",
       "      <td>8</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.655547</td>\n",
       "      <td>0.769471</td>\n",
       "      <td>0.711607</td>\n",
       "      <td>0.746181</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24873</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5.666666</td>\n",
       "      <td>11</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.770395</td>\n",
       "      <td>0.807835</td>\n",
       "      <td>0.910005</td>\n",
       "      <td>0.765564</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>phishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25087</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.725455</td>\n",
       "      <td>0.833899</td>\n",
       "      <td>0.742024</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.579380</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>phishing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13258</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.739307</td>\n",
       "      <td>0.818007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.785982</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.790019</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Querylength  domain_token_count  path_token_count  avgdomaintokenlen  \\\n",
       "1620            21                   2                 9          13.000000   \n",
       "14306            0                   3                12           4.333334   \n",
       "24873            0                   3                 5           5.666666   \n",
       "25087            0                   4                 8           7.000000   \n",
       "13258           20                   2                 7           6.000000   \n",
       "\n",
       "       longdomaintokenlen  avgpathtokenlen  tld  charcompvowels  charcompace  \\\n",
       "1620                   23         2.666667    2              17            9   \n",
       "14306                   8         3.666667    3              21           16   \n",
       "24873                  11         9.200000    3              10            9   \n",
       "25087                  11         3.500000    4               8            8   \n",
       "13258                  10         2.500000    2              11            6   \n",
       "\n",
       "       ldl_url  ...  SymbolCount_FileName  SymbolCount_Extension  \\\n",
       "1620         0  ...                     9                      8   \n",
       "14306        0  ...                     0                      0   \n",
       "24873        4  ...                     0                      0   \n",
       "25087        1  ...                     1                      0   \n",
       "13258        0  ...                     4                      0   \n",
       "\n",
       "       SymbolCount_Afterpath  Entropy_URL  Entropy_Domain  \\\n",
       "1620                       7     0.690780        0.750744   \n",
       "14306                     -1     0.655547        0.769471   \n",
       "24873                     -1     0.770395        0.807835   \n",
       "25087                     -1     0.725455        0.833899   \n",
       "13258                      3     0.739307        0.818007   \n",
       "\n",
       "       Entropy_DirectoryName  Entropy_Filename  Entropy_Extension  \\\n",
       "1620                     NaN          0.742480           0.751096   \n",
       "14306               0.711607          0.746181           0.000000   \n",
       "24873               0.910005          0.765564           0.000000   \n",
       "25087               0.742024          0.916667           0.579380   \n",
       "13258                    NaN          0.785982           0.000000   \n",
       "\n",
       "       Entropy_Afterpath  URL_Type_obf_Type  \n",
       "1620            0.742274         Defacement  \n",
       "14306          -1.000000             benign  \n",
       "24873          -1.000000           phishing  \n",
       "25087          -1.000000           phishing  \n",
       "13258           0.790019             benign  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./All.csv')\n",
    "df = df.sample(frac=1)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess\n",
    "### Content-Based Features\n",
    "These are obtained from the downloaded HTML code of the webpage. These features capture the structure of the webpage and the content embedded in it. These will include information on script tags, embedded objects, executables, hidden elements, etc.\n",
    "\n",
    "### Host-Based Features\n",
    "these are characteristics of the host-name properties of the URL. These provide information about the host of the webpage, for example, country of registration, domain name properties, open ports, named servers, connection speed, time to live from registration, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image info](https://miro.medium.com/max/1390/1*MNYb5o472rN9bQ4dmLDjTA.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Types of labels:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Defacement', 'benign', 'malware', 'phishing', 'spam'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Types of labels:\")\n",
    "np.unique(df['URL_Type_obf_Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels,counts = np.unique(df[\"URL_Type_obf_Type\"],return_counts=True)\n",
    "plt.bar(labels,counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['URL_Type_obf_Type'] = pd.Categorical(df['URL_Type_obf_Type'])\n",
    "df['URL_Type_obf_Type'] = df['URL_Type_obf_Type'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36707, 80)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling missing data\n",
    "We drop the features that have more than 20% of its total data missing (null)\n",
    "and the rest of the features that have some missing data, we drop the rows with null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "missing_data = df.isnull().sum()\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.set_ylabel('Count of missing data')\n",
    "plt.xticks(rotation=90)\n",
    "ax.bar(df.columns, missing_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.indexes.base.Index'>\n",
      "Features with more than 20% missing data are:\n",
      "Index(['NumberRate_Extension', 'Entropy_DirectoryName'], dtype='object')\n",
      "\n",
      "Number of rows with missing data: 585\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(36133, 78)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_null = df.isnull().sum() / len(df)\n",
    "missing_features = pct_null[pct_null > 0.20].index\n",
    "print(type(missing_features))\n",
    "print(f\"Features with more than 20% missing data are:\\n{missing_features}\")\n",
    "df.drop(missing_features, axis=1, inplace=True)\n",
    "missing_rows = df.isnull().values.ravel().sum()\n",
    "print(f\"\\nNumber of rows with missing data: {missing_rows}\")\n",
    "df.dropna(inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  background-color: #9dbdff;\n",
       "  color: #000000;\n",
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       "#T_7d7e5_row76_col0 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
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       "  background-color: #b40426;\n",
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       "}\n",
       "</style>\n",
       "<table id=\"T_7d7e5_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >URL_Type_obf_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row0\" class=\"row_heading level0 row0\" >Querylength</th>\n",
       "      <td id=\"T_7d7e5_row0_col0\" class=\"data row0 col0\" >0.135884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row1\" class=\"row_heading level0 row1\" >domain_token_count</th>\n",
       "      <td id=\"T_7d7e5_row1_col0\" class=\"data row1 col0\" >0.293984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row2\" class=\"row_heading level0 row2\" >path_token_count</th>\n",
       "      <td id=\"T_7d7e5_row2_col0\" class=\"data row2 col0\" >-0.079892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row3\" class=\"row_heading level0 row3\" >avgdomaintokenlen</th>\n",
       "      <td id=\"T_7d7e5_row3_col0\" class=\"data row3 col0\" >-0.121819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row4\" class=\"row_heading level0 row4\" >longdomaintokenlen</th>\n",
       "      <td id=\"T_7d7e5_row4_col0\" class=\"data row4 col0\" >-0.023160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row5\" class=\"row_heading level0 row5\" >avgpathtokenlen</th>\n",
       "      <td id=\"T_7d7e5_row5_col0\" class=\"data row5 col0\" >0.189884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row6\" class=\"row_heading level0 row6\" >tld</th>\n",
       "      <td id=\"T_7d7e5_row6_col0\" class=\"data row6 col0\" >0.293984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row7\" class=\"row_heading level0 row7\" >charcompvowels</th>\n",
       "      <td id=\"T_7d7e5_row7_col0\" class=\"data row7 col0\" >0.029943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row8\" class=\"row_heading level0 row8\" >charcompace</th>\n",
       "      <td id=\"T_7d7e5_row8_col0\" class=\"data row8 col0\" >0.151137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row9\" class=\"row_heading level0 row9\" >ldl_url</th>\n",
       "      <td id=\"T_7d7e5_row9_col0\" class=\"data row9 col0\" >0.249059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row10\" class=\"row_heading level0 row10\" >ldl_domain</th>\n",
       "      <td id=\"T_7d7e5_row10_col0\" class=\"data row10 col0\" >0.063738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row11\" class=\"row_heading level0 row11\" >ldl_path</th>\n",
       "      <td id=\"T_7d7e5_row11_col0\" class=\"data row11 col0\" >0.246233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row12\" class=\"row_heading level0 row12\" >ldl_filename</th>\n",
       "      <td id=\"T_7d7e5_row12_col0\" class=\"data row12 col0\" >0.015368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row13\" class=\"row_heading level0 row13\" >ldl_getArg</th>\n",
       "      <td id=\"T_7d7e5_row13_col0\" class=\"data row13 col0\" >0.212562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row14\" class=\"row_heading level0 row14\" >dld_url</th>\n",
       "      <td id=\"T_7d7e5_row14_col0\" class=\"data row14 col0\" >0.309024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row15\" class=\"row_heading level0 row15\" >dld_domain</th>\n",
       "      <td id=\"T_7d7e5_row15_col0\" class=\"data row15 col0\" >0.019557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row16\" class=\"row_heading level0 row16\" >dld_path</th>\n",
       "      <td id=\"T_7d7e5_row16_col0\" class=\"data row16 col0\" >0.309145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row17\" class=\"row_heading level0 row17\" >dld_filename</th>\n",
       "      <td id=\"T_7d7e5_row17_col0\" class=\"data row17 col0\" >-0.005776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row18\" class=\"row_heading level0 row18\" >dld_getArg</th>\n",
       "      <td id=\"T_7d7e5_row18_col0\" class=\"data row18 col0\" >0.265547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row19\" class=\"row_heading level0 row19\" >urlLen</th>\n",
       "      <td id=\"T_7d7e5_row19_col0\" class=\"data row19 col0\" >0.175546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row20\" class=\"row_heading level0 row20\" >domainlength</th>\n",
       "      <td id=\"T_7d7e5_row20_col0\" class=\"data row20 col0\" >0.114370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row21\" class=\"row_heading level0 row21\" >pathLength</th>\n",
       "      <td id=\"T_7d7e5_row21_col0\" class=\"data row21 col0\" >0.166982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row22\" class=\"row_heading level0 row22\" >subDirLen</th>\n",
       "      <td id=\"T_7d7e5_row22_col0\" class=\"data row22 col0\" >0.166982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row23\" class=\"row_heading level0 row23\" >fileNameLen</th>\n",
       "      <td id=\"T_7d7e5_row23_col0\" class=\"data row23 col0\" >-0.064342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row24\" class=\"row_heading level0 row24\" >this.fileExtLen</th>\n",
       "      <td id=\"T_7d7e5_row24_col0\" class=\"data row24 col0\" >0.058983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row25\" class=\"row_heading level0 row25\" >ArgLen</th>\n",
       "      <td id=\"T_7d7e5_row25_col0\" class=\"data row25 col0\" >0.138205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row26\" class=\"row_heading level0 row26\" >pathurlRatio</th>\n",
       "      <td id=\"T_7d7e5_row26_col0\" class=\"data row26 col0\" >-0.013792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row27\" class=\"row_heading level0 row27\" >ArgUrlRatio</th>\n",
       "      <td id=\"T_7d7e5_row27_col0\" class=\"data row27 col0\" >-0.049530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row28\" class=\"row_heading level0 row28\" >argDomanRatio</th>\n",
       "      <td id=\"T_7d7e5_row28_col0\" class=\"data row28 col0\" >0.147124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row29\" class=\"row_heading level0 row29\" >domainUrlRatio</th>\n",
       "      <td id=\"T_7d7e5_row29_col0\" class=\"data row29 col0\" >0.047262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row30\" class=\"row_heading level0 row30\" >pathDomainRatio</th>\n",
       "      <td id=\"T_7d7e5_row30_col0\" class=\"data row30 col0\" >0.141821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row31\" class=\"row_heading level0 row31\" >argPathRatio</th>\n",
       "      <td id=\"T_7d7e5_row31_col0\" class=\"data row31 col0\" >-0.096653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row32\" class=\"row_heading level0 row32\" >executable</th>\n",
       "      <td id=\"T_7d7e5_row32_col0\" class=\"data row32 col0\" >0.003019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row33\" class=\"row_heading level0 row33\" >isPortEighty</th>\n",
       "      <td id=\"T_7d7e5_row33_col0\" class=\"data row33 col0\" >-0.001179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row34\" class=\"row_heading level0 row34\" >NumberofDotsinURL</th>\n",
       "      <td id=\"T_7d7e5_row34_col0\" class=\"data row34 col0\" >0.248035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row35\" class=\"row_heading level0 row35\" >ISIpAddressInDomainName</th>\n",
       "      <td id=\"T_7d7e5_row35_col0\" class=\"data row35 col0\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row36\" class=\"row_heading level0 row36\" >CharacterContinuityRate</th>\n",
       "      <td id=\"T_7d7e5_row36_col0\" class=\"data row36 col0\" >-0.156883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row37\" class=\"row_heading level0 row37\" >LongestVariableValue</th>\n",
       "      <td id=\"T_7d7e5_row37_col0\" class=\"data row37 col0\" >0.162186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row38\" class=\"row_heading level0 row38\" >URL_DigitCount</th>\n",
       "      <td id=\"T_7d7e5_row38_col0\" class=\"data row38 col0\" >0.292593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row39\" class=\"row_heading level0 row39\" >host_DigitCount</th>\n",
       "      <td id=\"T_7d7e5_row39_col0\" class=\"data row39 col0\" >0.026020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row40\" class=\"row_heading level0 row40\" >Directory_DigitCount</th>\n",
       "      <td id=\"T_7d7e5_row40_col0\" class=\"data row40 col0\" >0.096148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row41\" class=\"row_heading level0 row41\" >File_name_DigitCount</th>\n",
       "      <td id=\"T_7d7e5_row41_col0\" class=\"data row41 col0\" >-0.018973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row42\" class=\"row_heading level0 row42\" >Extension_DigitCount</th>\n",
       "      <td id=\"T_7d7e5_row42_col0\" class=\"data row42 col0\" >0.286510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row43\" class=\"row_heading level0 row43\" >Query_DigitCount</th>\n",
       "      <td id=\"T_7d7e5_row43_col0\" class=\"data row43 col0\" >0.198837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row44\" class=\"row_heading level0 row44\" >URL_Letter_Count</th>\n",
       "      <td id=\"T_7d7e5_row44_col0\" class=\"data row44 col0\" >0.150335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row45\" class=\"row_heading level0 row45\" >host_letter_count</th>\n",
       "      <td id=\"T_7d7e5_row45_col0\" class=\"data row45 col0\" >0.084431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row46\" class=\"row_heading level0 row46\" >Directory_LetterCount</th>\n",
       "      <td id=\"T_7d7e5_row46_col0\" class=\"data row46 col0\" >0.246040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row47\" class=\"row_heading level0 row47\" >Filename_LetterCount</th>\n",
       "      <td id=\"T_7d7e5_row47_col0\" class=\"data row47 col0\" >-0.077613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row48\" class=\"row_heading level0 row48\" >Extension_LetterCount</th>\n",
       "      <td id=\"T_7d7e5_row48_col0\" class=\"data row48 col0\" >0.105864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row49\" class=\"row_heading level0 row49\" >Query_LetterCount</th>\n",
       "      <td id=\"T_7d7e5_row49_col0\" class=\"data row49 col0\" >0.124412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row50\" class=\"row_heading level0 row50\" >LongestPathTokenLength</th>\n",
       "      <td id=\"T_7d7e5_row50_col0\" class=\"data row50 col0\" >0.118660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row51\" class=\"row_heading level0 row51\" >Domain_LongestWordLength</th>\n",
       "      <td id=\"T_7d7e5_row51_col0\" class=\"data row51 col0\" >0.000987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row52\" class=\"row_heading level0 row52\" >Path_LongestWordLength</th>\n",
       "      <td id=\"T_7d7e5_row52_col0\" class=\"data row52 col0\" >0.005141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row53\" class=\"row_heading level0 row53\" >sub-Directory_LongestWordLength</th>\n",
       "      <td id=\"T_7d7e5_row53_col0\" class=\"data row53 col0\" >-0.017517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row54\" class=\"row_heading level0 row54\" >Arguments_LongestWordLength</th>\n",
       "      <td id=\"T_7d7e5_row54_col0\" class=\"data row54 col0\" >-0.235933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row55\" class=\"row_heading level0 row55\" >URL_sensitiveWord</th>\n",
       "      <td id=\"T_7d7e5_row55_col0\" class=\"data row55 col0\" >0.070371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row56\" class=\"row_heading level0 row56\" >URLQueries_variable</th>\n",
       "      <td id=\"T_7d7e5_row56_col0\" class=\"data row56 col0\" >-0.181833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row57\" class=\"row_heading level0 row57\" >spcharUrl</th>\n",
       "      <td id=\"T_7d7e5_row57_col0\" class=\"data row57 col0\" >0.286989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row58\" class=\"row_heading level0 row58\" >delimeter_Domain</th>\n",
       "      <td id=\"T_7d7e5_row58_col0\" class=\"data row58 col0\" >-0.039076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row59\" class=\"row_heading level0 row59\" >delimeter_path</th>\n",
       "      <td id=\"T_7d7e5_row59_col0\" class=\"data row59 col0\" >-0.217499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row60\" class=\"row_heading level0 row60\" >delimeter_Count</th>\n",
       "      <td id=\"T_7d7e5_row60_col0\" class=\"data row60 col0\" >-0.176648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row61\" class=\"row_heading level0 row61\" >NumberRate_URL</th>\n",
       "      <td id=\"T_7d7e5_row61_col0\" class=\"data row61 col0\" >0.240188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row62\" class=\"row_heading level0 row62\" >NumberRate_Domain</th>\n",
       "      <td id=\"T_7d7e5_row62_col0\" class=\"data row62 col0\" >-0.030870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row63\" class=\"row_heading level0 row63\" >NumberRate_DirectoryName</th>\n",
       "      <td id=\"T_7d7e5_row63_col0\" class=\"data row63 col0\" >0.089086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row64\" class=\"row_heading level0 row64\" >NumberRate_FileName</th>\n",
       "      <td id=\"T_7d7e5_row64_col0\" class=\"data row64 col0\" >0.216912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row65\" class=\"row_heading level0 row65\" >NumberRate_AfterPath</th>\n",
       "      <td id=\"T_7d7e5_row65_col0\" class=\"data row65 col0\" >-0.012598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row66\" class=\"row_heading level0 row66\" >SymbolCount_URL</th>\n",
       "      <td id=\"T_7d7e5_row66_col0\" class=\"data row66 col0\" >0.077296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row67\" class=\"row_heading level0 row67\" >SymbolCount_Domain</th>\n",
       "      <td id=\"T_7d7e5_row67_col0\" class=\"data row67 col0\" >0.294352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row68\" class=\"row_heading level0 row68\" >SymbolCount_Directoryname</th>\n",
       "      <td id=\"T_7d7e5_row68_col0\" class=\"data row68 col0\" >0.352774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row69\" class=\"row_heading level0 row69\" >SymbolCount_FileName</th>\n",
       "      <td id=\"T_7d7e5_row69_col0\" class=\"data row69 col0\" >-0.102449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row70\" class=\"row_heading level0 row70\" >SymbolCount_Extension</th>\n",
       "      <td id=\"T_7d7e5_row70_col0\" class=\"data row70 col0\" >-0.126021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row71\" class=\"row_heading level0 row71\" >SymbolCount_Afterpath</th>\n",
       "      <td id=\"T_7d7e5_row71_col0\" class=\"data row71 col0\" >-0.151154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row72\" class=\"row_heading level0 row72\" >Entropy_URL</th>\n",
       "      <td id=\"T_7d7e5_row72_col0\" class=\"data row72 col0\" >0.007225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row73\" class=\"row_heading level0 row73\" >Entropy_Domain</th>\n",
       "      <td id=\"T_7d7e5_row73_col0\" class=\"data row73 col0\" >-0.052724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row74\" class=\"row_heading level0 row74\" >Entropy_Filename</th>\n",
       "      <td id=\"T_7d7e5_row74_col0\" class=\"data row74 col0\" >0.128739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row75\" class=\"row_heading level0 row75\" >Entropy_Extension</th>\n",
       "      <td id=\"T_7d7e5_row75_col0\" class=\"data row75 col0\" >0.094543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row76\" class=\"row_heading level0 row76\" >Entropy_Afterpath</th>\n",
       "      <td id=\"T_7d7e5_row76_col0\" class=\"data row76 col0\" >-0.044560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7d7e5_level0_row77\" class=\"row_heading level0 row77\" >URL_Type_obf_Type</th>\n",
       "      <td id=\"T_7d7e5_row77_col0\" class=\"data row77 col0\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fab55a6f550>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "corr_label = corr[['URL_Type_obf_Type']]\n",
    "corr_label.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML Process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((36133, 77), (36133,))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(['URL_Type_obf_Type'], axis=1)\n",
    "y = df['URL_Type_obf_Type']\n",
    "X = X.to_numpy()\n",
    "y = y.to_numpy()\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) \n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = pd.Series(y_train).value_counts().values / y_train.size\n",
    "all_weights = np.zeros(y_train.size)\n",
    "for label in np.unique(y_train):\n",
    "    all_weights[y_train == label] = weights[label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:06:07] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
      "Parameters: { use_label_encoder } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mlogloss:1.52622\tvalidation_1-mlogloss:1.52622\n",
      "Multiple eval metrics have been passed: 'validation_1-mlogloss' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mlogloss hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mlogloss:1.45094\tvalidation_1-mlogloss:1.45074\n",
      "[2]\tvalidation_0-mlogloss:1.38297\tvalidation_1-mlogloss:1.38359\n",
      "[3]\tvalidation_0-mlogloss:1.32158\tvalidation_1-mlogloss:1.32226\n",
      "[4]\tvalidation_0-mlogloss:1.26517\tvalidation_1-mlogloss:1.26551\n",
      "[5]\tvalidation_0-mlogloss:1.21345\tvalidation_1-mlogloss:1.21364\n",
      "[6]\tvalidation_0-mlogloss:1.16611\tvalidation_1-mlogloss:1.16585\n",
      "[7]\tvalidation_0-mlogloss:1.12146\tvalidation_1-mlogloss:1.12113\n",
      "[8]\tvalidation_0-mlogloss:1.07791\tvalidation_1-mlogloss:1.07744\n",
      "[9]\tvalidation_0-mlogloss:1.03804\tvalidation_1-mlogloss:1.03751\n",
      "[10]\tvalidation_0-mlogloss:1.00044\tvalidation_1-mlogloss:1.00012\n",
      "[11]\tvalidation_0-mlogloss:0.96521\tvalidation_1-mlogloss:0.96471\n",
      "[12]\tvalidation_0-mlogloss:0.93171\tvalidation_1-mlogloss:0.93141\n",
      "[13]\tvalidation_0-mlogloss:0.90104\tvalidation_1-mlogloss:0.90069\n",
      "[14]\tvalidation_0-mlogloss:0.87232\tvalidation_1-mlogloss:0.87235\n",
      "[15]\tvalidation_0-mlogloss:0.84453\tvalidation_1-mlogloss:0.84441\n",
      "[16]\tvalidation_0-mlogloss:0.81804\tvalidation_1-mlogloss:0.81782\n",
      "[17]\tvalidation_0-mlogloss:0.79231\tvalidation_1-mlogloss:0.79217\n",
      "[18]\tvalidation_0-mlogloss:0.76918\tvalidation_1-mlogloss:0.76916\n",
      "[19]\tvalidation_0-mlogloss:0.74662\tvalidation_1-mlogloss:0.74648\n",
      "[20]\tvalidation_0-mlogloss:0.72525\tvalidation_1-mlogloss:0.72496\n",
      "[21]\tvalidation_0-mlogloss:0.70463\tvalidation_1-mlogloss:0.70423\n",
      "[22]\tvalidation_0-mlogloss:0.68442\tvalidation_1-mlogloss:0.68408\n",
      "[23]\tvalidation_0-mlogloss:0.66531\tvalidation_1-mlogloss:0.66517\n",
      "[24]\tvalidation_0-mlogloss:0.64763\tvalidation_1-mlogloss:0.64772\n",
      "[25]\tvalidation_0-mlogloss:0.63065\tvalidation_1-mlogloss:0.63100\n",
      "[26]\tvalidation_0-mlogloss:0.61356\tvalidation_1-mlogloss:0.61407\n",
      "[27]\tvalidation_0-mlogloss:0.59734\tvalidation_1-mlogloss:0.59813\n",
      "[28]\tvalidation_0-mlogloss:0.58173\tvalidation_1-mlogloss:0.58294\n",
      "[29]\tvalidation_0-mlogloss:0.56746\tvalidation_1-mlogloss:0.56876\n",
      "[30]\tvalidation_0-mlogloss:0.55336\tvalidation_1-mlogloss:0.55497\n",
      "[31]\tvalidation_0-mlogloss:0.53944\tvalidation_1-mlogloss:0.54099\n",
      "[32]\tvalidation_0-mlogloss:0.52667\tvalidation_1-mlogloss:0.52848\n",
      "[33]\tvalidation_0-mlogloss:0.51446\tvalidation_1-mlogloss:0.51634\n",
      "[34]\tvalidation_0-mlogloss:0.50203\tvalidation_1-mlogloss:0.50386\n",
      "[35]\tvalidation_0-mlogloss:0.49005\tvalidation_1-mlogloss:0.49203\n",
      "[36]\tvalidation_0-mlogloss:0.47853\tvalidation_1-mlogloss:0.48068\n",
      "[37]\tvalidation_0-mlogloss:0.46802\tvalidation_1-mlogloss:0.47051\n",
      "[38]\tvalidation_0-mlogloss:0.45726\tvalidation_1-mlogloss:0.45989\n",
      "[39]\tvalidation_0-mlogloss:0.44720\tvalidation_1-mlogloss:0.44996\n",
      "[40]\tvalidation_0-mlogloss:0.43712\tvalidation_1-mlogloss:0.44026\n",
      "[41]\tvalidation_0-mlogloss:0.42712\tvalidation_1-mlogloss:0.43058\n",
      "[42]\tvalidation_0-mlogloss:0.41789\tvalidation_1-mlogloss:0.42152\n",
      "[43]\tvalidation_0-mlogloss:0.40882\tvalidation_1-mlogloss:0.41278\n",
      "[44]\tvalidation_0-mlogloss:0.40001\tvalidation_1-mlogloss:0.40440\n",
      "[45]\tvalidation_0-mlogloss:0.39173\tvalidation_1-mlogloss:0.39635\n",
      "[46]\tvalidation_0-mlogloss:0.38352\tvalidation_1-mlogloss:0.38852\n",
      "[47]\tvalidation_0-mlogloss:0.37586\tvalidation_1-mlogloss:0.38107\n",
      "[48]\tvalidation_0-mlogloss:0.36885\tvalidation_1-mlogloss:0.37436\n",
      "[49]\tvalidation_0-mlogloss:0.36174\tvalidation_1-mlogloss:0.36750\n",
      "[50]\tvalidation_0-mlogloss:0.35413\tvalidation_1-mlogloss:0.36026\n",
      "[51]\tvalidation_0-mlogloss:0.34799\tvalidation_1-mlogloss:0.35448\n",
      "[52]\tvalidation_0-mlogloss:0.34183\tvalidation_1-mlogloss:0.34854\n",
      "[53]\tvalidation_0-mlogloss:0.33583\tvalidation_1-mlogloss:0.34261\n",
      "[54]\tvalidation_0-mlogloss:0.32962\tvalidation_1-mlogloss:0.33674\n",
      "[55]\tvalidation_0-mlogloss:0.32382\tvalidation_1-mlogloss:0.33127\n",
      "[56]\tvalidation_0-mlogloss:0.31776\tvalidation_1-mlogloss:0.32555\n",
      "[57]\tvalidation_0-mlogloss:0.31224\tvalidation_1-mlogloss:0.32040\n",
      "[58]\tvalidation_0-mlogloss:0.30604\tvalidation_1-mlogloss:0.31443\n",
      "[59]\tvalidation_0-mlogloss:0.30059\tvalidation_1-mlogloss:0.30910\n",
      "[60]\tvalidation_0-mlogloss:0.29550\tvalidation_1-mlogloss:0.30416\n",
      "[61]\tvalidation_0-mlogloss:0.29008\tvalidation_1-mlogloss:0.29890\n",
      "[62]\tvalidation_0-mlogloss:0.28558\tvalidation_1-mlogloss:0.29458\n",
      "[63]\tvalidation_0-mlogloss:0.28050\tvalidation_1-mlogloss:0.28958\n",
      "[64]\tvalidation_0-mlogloss:0.27579\tvalidation_1-mlogloss:0.28490\n",
      "[65]\tvalidation_0-mlogloss:0.27153\tvalidation_1-mlogloss:0.28087\n",
      "[66]\tvalidation_0-mlogloss:0.26743\tvalidation_1-mlogloss:0.27700\n",
      "[67]\tvalidation_0-mlogloss:0.26328\tvalidation_1-mlogloss:0.27314\n",
      "[68]\tvalidation_0-mlogloss:0.25930\tvalidation_1-mlogloss:0.26946\n",
      "[69]\tvalidation_0-mlogloss:0.25517\tvalidation_1-mlogloss:0.26549\n",
      "[70]\tvalidation_0-mlogloss:0.25149\tvalidation_1-mlogloss:0.26212\n",
      "[71]\tvalidation_0-mlogloss:0.24771\tvalidation_1-mlogloss:0.25863\n",
      "[72]\tvalidation_0-mlogloss:0.24410\tvalidation_1-mlogloss:0.25531\n",
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      "[964]\tvalidation_0-mlogloss:0.00943\tvalidation_1-mlogloss:0.05953\n",
      "[965]\tvalidation_0-mlogloss:0.00942\tvalidation_1-mlogloss:0.05953\n",
      "[966]\tvalidation_0-mlogloss:0.00941\tvalidation_1-mlogloss:0.05953\n",
      "[967]\tvalidation_0-mlogloss:0.00939\tvalidation_1-mlogloss:0.05950\n",
      "[968]\tvalidation_0-mlogloss:0.00938\tvalidation_1-mlogloss:0.05950\n",
      "[969]\tvalidation_0-mlogloss:0.00936\tvalidation_1-mlogloss:0.05953\n",
      "[970]\tvalidation_0-mlogloss:0.00935\tvalidation_1-mlogloss:0.05952\n",
      "[971]\tvalidation_0-mlogloss:0.00934\tvalidation_1-mlogloss:0.05954\n",
      "[972]\tvalidation_0-mlogloss:0.00932\tvalidation_1-mlogloss:0.05954\n",
      "[973]\tvalidation_0-mlogloss:0.00931\tvalidation_1-mlogloss:0.05952\n",
      "[974]\tvalidation_0-mlogloss:0.00930\tvalidation_1-mlogloss:0.05952\n",
      "[975]\tvalidation_0-mlogloss:0.00928\tvalidation_1-mlogloss:0.05952\n",
      "[976]\tvalidation_0-mlogloss:0.00927\tvalidation_1-mlogloss:0.05949\n",
      "[977]\tvalidation_0-mlogloss:0.00926\tvalidation_1-mlogloss:0.05952\n",
      "[978]\tvalidation_0-mlogloss:0.00924\tvalidation_1-mlogloss:0.05954\n",
      "[979]\tvalidation_0-mlogloss:0.00923\tvalidation_1-mlogloss:0.05955\n",
      "[980]\tvalidation_0-mlogloss:0.00922\tvalidation_1-mlogloss:0.05952\n",
      "[981]\tvalidation_0-mlogloss:0.00921\tvalidation_1-mlogloss:0.05951\n",
      "[982]\tvalidation_0-mlogloss:0.00920\tvalidation_1-mlogloss:0.05950\n",
      "[983]\tvalidation_0-mlogloss:0.00918\tvalidation_1-mlogloss:0.05954\n",
      "[984]\tvalidation_0-mlogloss:0.00917\tvalidation_1-mlogloss:0.05953\n",
      "[985]\tvalidation_0-mlogloss:0.00916\tvalidation_1-mlogloss:0.05955\n",
      "[986]\tvalidation_0-mlogloss:0.00914\tvalidation_1-mlogloss:0.05955\n",
      "Stopping. Best iteration:\n",
      "[976]\tvalidation_0-mlogloss:0.00927\tvalidation_1-mlogloss:0.05949\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb = XGBClassifier(n_estimators=5000, learning_rate=0.05, gamma=0, subsample=0.7,\n",
    "                            objective=\"multi:softprob\",\n",
    "                            num_class=len(np.unique(y_train)),\n",
    "                            eval_metric=\"mlogloss\", random_state=93, use_label_encoder=False)\n",
    "\n",
    "\n",
    "xgb = xgb.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)],\n",
    "                        early_stopping_rounds=10,\n",
    "                        sample_weight=all_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1579\n",
      "           1       0.99      0.99      0.99      1480\n",
      "           2       0.98      0.98      0.98      1315\n",
      "           3       0.97      0.97      0.97      1454\n",
      "           4       0.99      0.99      0.99      1399\n",
      "\n",
      "    accuracy                           0.98      7227\n",
      "   macro avg       0.98      0.98      0.98      7227\n",
      "weighted avg       0.98      0.98      0.98      7227\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# predict classification report\n",
    "y_pred = xgb.predict(X_test)\n",
    "# print classification report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Accuracy: 98.37%\n"
     ]
    }
   ],
   "source": [
    "predictions = [round(value) for value in y_pred]\n",
    "xgb_accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"XGBoost Accuracy: %.2f%%\" % (xgb_accuracy * 100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"xgb_good\", \"wb\") as file:\n",
    "    pickle.dump(xgb, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_binary = df.copy()\n",
    "df_binary['label'] = df_binary['URL_Type_obf_Type'].map({'benign':0, 'malware':1,'Defacement':1, 'spam':1, 'phishing':1})\n",
    "df_binary.drop(['URL_Type_obf_Type'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    28926\n",
      "0     7781\n",
      "Name: label, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fab70cc96d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df_binary['label'].value_counts())\n",
    "df_binary['label'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((36707, 79), (36707,))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_binary = df_binary.drop(['label'], axis=1)\n",
    "y_binary = df_binary['label']\n",
    "X_binary = X_binary.to_numpy()\n",
    "y_binary = y_binary.to_numpy()\n",
    "X_binary.shape, y_binary.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_binary, y_binary, test_size=0.2, random_state=42) \n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:31:11] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480: \n",
      "Parameters: { use_label_encoder } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mlogloss:0.65303\tvalidation_1-mlogloss:0.65307\n",
      "Multiple eval metrics have been passed: 'validation_1-mlogloss' will be used for early stopping.\n",
      "\n",
      "Will train until validation_1-mlogloss hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-mlogloss:0.61700\tvalidation_1-mlogloss:0.61708\n",
      "[2]\tvalidation_0-mlogloss:0.58413\tvalidation_1-mlogloss:0.58419\n",
      "[3]\tvalidation_0-mlogloss:0.55422\tvalidation_1-mlogloss:0.55432\n",
      "[4]\tvalidation_0-mlogloss:0.52700\tvalidation_1-mlogloss:0.52708\n",
      "[5]\tvalidation_0-mlogloss:0.50184\tvalidation_1-mlogloss:0.50186\n",
      "[6]\tvalidation_0-mlogloss:0.47879\tvalidation_1-mlogloss:0.47878\n",
      "[7]\tvalidation_0-mlogloss:0.45733\tvalidation_1-mlogloss:0.45743\n",
      "[8]\tvalidation_0-mlogloss:0.43762\tvalidation_1-mlogloss:0.43760\n",
      "[9]\tvalidation_0-mlogloss:0.41899\tvalidation_1-mlogloss:0.41894\n",
      "[10]\tvalidation_0-mlogloss:0.40176\tvalidation_1-mlogloss:0.40174\n",
      "[11]\tvalidation_0-mlogloss:0.38626\tvalidation_1-mlogloss:0.38611\n",
      "[12]\tvalidation_0-mlogloss:0.37158\tvalidation_1-mlogloss:0.37142\n",
      "[13]\tvalidation_0-mlogloss:0.35678\tvalidation_1-mlogloss:0.35664\n",
      "[14]\tvalidation_0-mlogloss:0.34367\tvalidation_1-mlogloss:0.34353\n",
      "[15]\tvalidation_0-mlogloss:0.33125\tvalidation_1-mlogloss:0.33121\n",
      "[16]\tvalidation_0-mlogloss:0.31927\tvalidation_1-mlogloss:0.31935\n",
      "[17]\tvalidation_0-mlogloss:0.30718\tvalidation_1-mlogloss:0.30745\n",
      "[18]\tvalidation_0-mlogloss:0.29668\tvalidation_1-mlogloss:0.29681\n",
      "[19]\tvalidation_0-mlogloss:0.28604\tvalidation_1-mlogloss:0.28621\n",
      "[20]\tvalidation_0-mlogloss:0.27658\tvalidation_1-mlogloss:0.27676\n",
      "[21]\tvalidation_0-mlogloss:0.26655\tvalidation_1-mlogloss:0.26687\n",
      "[22]\tvalidation_0-mlogloss:0.25693\tvalidation_1-mlogloss:0.25745\n",
      "[23]\tvalidation_0-mlogloss:0.24885\tvalidation_1-mlogloss:0.24944\n",
      "[24]\tvalidation_0-mlogloss:0.24111\tvalidation_1-mlogloss:0.24168\n",
      "[25]\tvalidation_0-mlogloss:0.23303\tvalidation_1-mlogloss:0.23362\n",
      "[26]\tvalidation_0-mlogloss:0.22654\tvalidation_1-mlogloss:0.22708\n",
      "[27]\tvalidation_0-mlogloss:0.21905\tvalidation_1-mlogloss:0.21987\n",
      "[28]\tvalidation_0-mlogloss:0.21136\tvalidation_1-mlogloss:0.21251\n",
      "[29]\tvalidation_0-mlogloss:0.20522\tvalidation_1-mlogloss:0.20650\n",
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      "[492]\tvalidation_0-mlogloss:0.00539\tvalidation_1-mlogloss:0.02076\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[493]\tvalidation_0-mlogloss:0.00537\tvalidation_1-mlogloss:0.02075\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[618]\tvalidation_0-mlogloss:0.00350\tvalidation_1-mlogloss:0.01932\n",
      "[619]\tvalidation_0-mlogloss:0.00349\tvalidation_1-mlogloss:0.01932\n",
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      "[642]\tvalidation_0-mlogloss:0.00328\tvalidation_1-mlogloss:0.01914\n",
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      "[649]\tvalidation_0-mlogloss:0.00321\tvalidation_1-mlogloss:0.01916\n",
      "[650]\tvalidation_0-mlogloss:0.00321\tvalidation_1-mlogloss:0.01916\n",
      "Stopping. Best iteration:\n",
      "[640]\tvalidation_0-mlogloss:0.00330\tvalidation_1-mlogloss:0.01912\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb = XGBClassifier(n_estimators=5000, learning_rate=0.05, gamma=0, subsample=0.7,\n",
    "                            objective=\"multi:softprob\",\n",
    "                            num_class=len(np.unique(y_train)),\n",
    "                            eval_metric=\"mlogloss\", random_state=93, use_label_encoder=False)\n",
    "\n",
    "\n",
    "xgb = xgb.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)],\n",
    "                        early_stopping_rounds=10,\n",
    "                        sample_weight=all_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1567\n",
      "           1       1.00      1.00      1.00      5775\n",
      "\n",
      "    accuracy                           0.99      7342\n",
      "   macro avg       0.99      0.99      0.99      7342\n",
      "weighted avg       0.99      0.99      0.99      7342\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# predict classification report\n",
    "y_pred_binary = xgb.predict(X_test)\n",
    "# print classification report\n",
    "print(classification_report(y_test, y_pred_binary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# strategy = tf.distribute.MirroredStrategy()\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential,Model\n",
    "from tensorflow.keras.layers import Dense,Dropout,Flatten\n",
    "model = Sequential()\n",
    "model.add(Dense(60, activation=\"relu\",input_shape=(77,)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(30, activation=\"relu\"))\n",
    "model.add(Dense(15, activation=\"relu\"))\n",
    "model.add(Dense(5, activation=\"softmax\"))\n",
    "model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),metrics=[\"accuracy\",tf.keras.metrics.Recall(),tf.keras.metrics.Precision()])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.utils import np_utils\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(y)\n",
    "encoded_Y = encoder.transform(y)\n",
    "y = np_utils.to_categorical(encoded_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) \n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=20)\n",
    "history = model.fit(X_train,y_train,batch_size=200,epochs=2500,\n",
    "                    validation_split=0.05,\n",
    "                    use_multiprocessing=True,workers=8,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "score_list=[]\n",
    "for i in range(1,30):\n",
    "    knnPlot = KNeighborsClassifier(n_neighbors=i)\n",
    "    knnPlot.fit(X_train,y_train)\n",
    "    score_list.append(knnPlot.score(X_test,y_test))\n",
    "\n",
    "plt.plot(range(1,30),score_list)\n",
    "plt.xlabel('#K - values', fontweight=\"bold\")\n",
    "plt.ylabel('Accuracy', fontweight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------K-nearest neighbors Report---------------\n",
      "F1 score: 0.9465764869493076\n",
      "Accuracy score: 0.9468659194686592\n",
      "Confusion matrix: \n",
      " [[1527    8    6   31    7]\n",
      " [  12 1402   29   24   13]\n",
      " [   7    4 1290   12    2]\n",
      " [  53   43   33 1283   42]\n",
      " [  16    9    3   30 1341]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96      1579\n",
      "           1       0.96      0.95      0.95      1480\n",
      "           2       0.95      0.98      0.96      1315\n",
      "           3       0.93      0.88      0.91      1454\n",
      "           4       0.95      0.96      0.96      1399\n",
      "\n",
      "    accuracy                           0.95      7227\n",
      "   macro avg       0.95      0.95      0.95      7227\n",
      "weighted avg       0.95      0.95      0.95      7227\n",
      "\n",
      "Cross Validation Scores: [0.90387275 0.90525588 0.89342561 0.90034602 0.89411765]\n",
      "Cross Validation Scores Mean: 0.8994035808123592 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "from sklearn import metrics\n",
    "\n",
    "max_neighbor = np.argmax(score_list)+1\n",
    "knn = KNeighborsClassifier(n_neighbors = max_neighbor)\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred_knn = knn.predict(X_test)\n",
    "knn_f1 = metrics.f1_score(y_test, y_pred_knn, average= \"weighted\")\n",
    "knn_accuracy = metrics.accuracy_score(y_test, y_pred_knn)\n",
    "knn_cm = metrics.confusion_matrix(y_test, y_pred_knn)\n",
    "print(\"-----------------K-nearest neighbors Report---------------\")\n",
    "print(\"F1 score: {}\".format(knn_f1))\n",
    "print(\"Accuracy score: {}\".format(knn_accuracy))\n",
    "print(\"Confusion matrix: \\n\", knn_cm)\n",
    "print(metrics.classification_report(y_test, y_pred_knn))\n",
    "kf = KFold(n_splits=5,shuffle=True, random_state=123)\n",
    "cv_scores = cross_val_score(knn,X_test, y_test, cv=kf)\n",
    "print(\"Cross Validation Scores:\", cv_scores)\n",
    "print(\"Cross Validation Scores Mean:\", cv_scores.mean(),\"\\n\")\n",
    "knn_mean_score = cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------Random Forest------------------\n",
      "F1 score: 0.9697773780408786\n",
      "Accuracy score: 0.9696969696969697\n",
      "Confusion matrix: \n",
      " [[1535    4    2   34    4]\n",
      " [   2 1453    4   19    2]\n",
      " [   4    5 1286   19    1]\n",
      " [  21   33   10 1381    9]\n",
      " [  11    1    3   31 1353]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.97      0.97      1579\n",
      "           1       0.97      0.98      0.98      1480\n",
      "           2       0.99      0.98      0.98      1315\n",
      "           3       0.93      0.95      0.94      1454\n",
      "           4       0.99      0.97      0.98      1399\n",
      "\n",
      "    accuracy                           0.97      7227\n",
      "   macro avg       0.97      0.97      0.97      7227\n",
      "weighted avg       0.97      0.97      0.97      7227\n",
      "\n",
      "Cross Validation Scores: [0.93153527 0.93775934 0.94048443 0.93356401 0.93079585]\n",
      "Cross Validation Scores Mean: 0.9348277792933135 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(random_state=0,n_jobs=-1)\n",
    "rf.fit(X_train,y_train)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "rf_f1 = metrics.f1_score(y_test, y_pred_rf, average= \"weighted\")\n",
    "rf_accuracy = metrics.accuracy_score(y_test, y_pred_rf)\n",
    "rf_cm = metrics.confusion_matrix(y_test, y_pred_rf)\n",
    "print(\"--------------------Random Forest------------------\")\n",
    "print(\"F1 score: {}\".format(rf_f1))\n",
    "print(\"Accuracy score: {}\".format(rf_accuracy))\n",
    "print(\"Confusion matrix: \\n\", rf_cm)\n",
    "print(metrics.classification_report(y_test, y_pred_rf))\n",
    "kf = KFold(n_splits=5,shuffle=True, random_state=123)\n",
    "cv_scores = cross_val_score(rf, X_test, y_test, cv=kf)\n",
    "print(\"Cross Validation Scores:\", cv_scores)\n",
    "print(\"Cross Validation Scores Mean:\", cv_scores.mean(),\"\\n\")\n",
    "rf_mean_score = cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------Logistic Regression------------------\n",
      "F1 score: 0.8234684322773951\n",
      "Accuracy score: 0.8257921682579217\n",
      "Confusion matrix: \n",
      " [[1337   48   48  104   42]\n",
      " [  28 1301   87   51   13]\n",
      " [ 110  228  825  132   20]\n",
      " [  42   45   69 1254   44]\n",
      " [  78    4   22   44 1251]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.85      0.84      1579\n",
      "           1       0.80      0.88      0.84      1480\n",
      "           2       0.78      0.63      0.70      1315\n",
      "           3       0.79      0.86      0.83      1454\n",
      "           4       0.91      0.89      0.90      1399\n",
      "\n",
      "    accuracy                           0.83      7227\n",
      "   macro avg       0.83      0.82      0.82      7227\n",
      "weighted avg       0.83      0.83      0.82      7227\n",
      "\n",
      "Cross Validation Scores: [0.81396957 0.81396957 0.82768166 0.81522491 0.81384083]\n",
      "Cross Validation Scores Mean: 0.816937309461251 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lgr = LogisticRegression()\n",
    "lgr.fit(X_train,y_train)\n",
    "y_pred_lgr = lgr.predict(X_test)\n",
    "lgr_f1 = metrics.f1_score(y_test, y_pred_lgr, average= \"weighted\")\n",
    "lgr_accuracy = metrics.accuracy_score(y_test, y_pred_lgr)\n",
    "lgr_cm = metrics.confusion_matrix(y_test, y_pred_lgr)\n",
    "print(\"--------------------Logistic Regression------------------\")\n",
    "print(\"F1 score: {}\".format(lgr_f1))\n",
    "print(\"Accuracy score: {}\".format(lgr_accuracy))\n",
    "print(\"Confusion matrix: \\n\", lgr_cm)\n",
    "print(metrics.classification_report(y_test, y_pred_lgr))\n",
    "kf = KFold(n_splits=5,shuffle=True, random_state=123)\n",
    "cv_scores = cross_val_score(lgr, X_test, y_test, cv=kf)\n",
    "print(\"Cross Validation Scores:\", cv_scores)\n",
    "print(\"Cross Validation Scores Mean:\", cv_scores.mean(),\"\\n\")\n",
    "lgr_mean_score = cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------Decision Tree------------------\n",
      "F1 score: 0.9514458054185977\n",
      "Accuracy score: 0.9517088695170887\n",
      "Confusion matrix: \n",
      " [[1523    6    7   33   10]\n",
      " [   7 1436   13   23    1]\n",
      " [   2    6 1286   18    3]\n",
      " [  49   29   57 1283   36]\n",
      " [  10    5    5   29 1350]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96      1579\n",
      "           1       0.97      0.97      0.97      1480\n",
      "           2       0.94      0.98      0.96      1315\n",
      "           3       0.93      0.88      0.90      1454\n",
      "           4       0.96      0.96      0.96      1399\n",
      "\n",
      "    accuracy                           0.95      7227\n",
      "   macro avg       0.95      0.95      0.95      7227\n",
      "weighted avg       0.95      0.95      0.95      7227\n",
      "\n",
      "Cross Validation Scores: [0.91009682 0.90318119 0.89757785 0.90380623 0.91141869]\n",
      "Cross Validation Scores Mean: 0.905216155292969 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "decsion_tree_model = DecisionTreeClassifier(random_state=42)\n",
    "decsion_tree_model.fit(X_train, y_train)\n",
    "y_pred_dt = decsion_tree_model.predict(X_test)\n",
    "dt_f1 = metrics.f1_score(y_test, y_pred_dt, average= \"weighted\")\n",
    "dt_accuracy = metrics.accuracy_score(y_test, y_pred_dt)\n",
    "dt_cm = metrics.confusion_matrix(y_test, y_pred_dt)\n",
    "print(\"--------------------Decision Tree------------------\")\n",
    "print(\"F1 score: {}\".format(dt_f1))\n",
    "print(\"Accuracy score: {}\".format(dt_accuracy))\n",
    "print(\"Confusion matrix: \\n\", dt_cm)\n",
    "print(metrics.classification_report(y_test, y_pred_dt))\n",
    "kf = KFold(n_splits=5,shuffle=True, random_state=123)\n",
    "cv_scores = cross_val_score(decsion_tree_model, X_test, y_test, cv=kf)\n",
    "print(\"Cross Validation Scores:\", cv_scores)\n",
    "print(\"Cross Validation Scores Mean:\", cv_scores.mean(),\"\\n\")\n",
    "dt_mean_score = cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9312301093123011"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "clf = MLPClassifier(random_state=1)\n",
    "clf.fit(X_train, y_train)\n",
    "clf.predict(X_test)\n",
    "clf.loss_\n",
    "mlp_score = clf.score(X_test, y_test)\n",
    "mlp_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['KNN', 'Logistic Regression', 'Decision Tree', 'MLP Classifier', 'Random Forest', 'XGBoost']\n",
    "all_model_scores = [knn_mean_score, lgr_mean_score, dt_mean_score, mlp_score, rf_mean_score, xgb_accuracy]\n",
    "\n",
    "x = np.arange(len(labels))\n",
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "models = ax.bar(x, all_model_scores, width)\n",
    "ax.set_title('Machine Learning Models Comparison', fontweight=\"bold\")\n",
    "ax.set_xlabel('Model Name', fontweight=\"bold\")\n",
    "ax.set_ylabel('Accuaracy %', fontweight=\"bold\")\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "def autolabel(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{:.3}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width()/2, height),\n",
    "                    xytext=(0, 3), \n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "autolabel(models)\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('./All.csv')\n",
    "df = df.sample(frac=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "labels,counts = np.unique(df[\"URL_Type_obf_Type\"],return_counts=True)\n",
    "plt.bar(labels,counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nans[(nans < 1000) & (nans > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nans = df.isna().sum()\n",
    "df = df.drop(list(nans[nans > 5000].index),axis=1)\n",
    "df = df.dropna()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['URL_Type_obf_Type'] = pd.Categorical(df['URL_Type_obf_Type'])\n",
    "df['URL_Type_obf_Type'] = df['URL_Type_obf_Type'].cat.codes\n",
    "X = df.drop(['URL_Type_obf_Type'], axis=1)\n",
    "y = df['URL_Type_obf_Type']\n",
    "X = X.to_numpy()\n",
    "y = y.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) \n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = pd.Series(y_train).value_counts().values / y_train.size\n",
    "all_weights = np.zeros(y_train.size)\n",
    "for label in np.unique(y_train):\n",
    "    all_weights[y_train == label] = weights[label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# strategy = tf.distribute.MirroredStrategy()\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential,Model\n",
    "from tensorflow.keras.layers import Dense,Dropout,Flatten\n",
    "model = Sequential()\n",
    "model.add(Dense(60, activation=\"relu\",input_shape=(77,)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(30, activation=\"relu\"))\n",
    "model.add(Dense(15, activation=\"relu\"))\n",
    "model.add(Dense(5, activation=\"softmax\"))\n",
    "model.compile(loss=tf.keras.losses.CategoricalCrossentropy(), optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),metrics=[\"accuracy\",tf.keras.metrics.Recall(),tf.keras.metrics.Precision()])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from keras.utils import np_utils\n",
    "y = np_utils.to_categorical(LabelEncoder().fit_transform(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) \n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=20)\n",
    "history = model.fit(X_train,y_train,batch_size=200,epochs=2500,\n",
    "                    validation_split=0.05,\n",
    "                    use_multiprocessing=True,workers=8,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"mlp_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test.argmax(axis=1),y_pred.argmax(axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "interpreter": {
   "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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  "language_info": {
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   "file_extension": ".py",
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   "base_numbering": 1,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
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     "varRefreshCmd": "cat(var_dic_list()) "
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   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}