CSC215_Project3_ExtraFeatures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import shutil\n",
    "import os\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "# Encode text values to dummy variables(i.e. [1,0,0],[0,1,0],[0,0,1] for red,green,blue)\n",
    "def encode_text_dummy(df, name):\n",
    "    dummies = pd.get_dummies(df[name])\n",
    "    for x in dummies.columns:\n",
    "        dummy_name = \"{}-{}\".format(name, x)\n",
    "        df[dummy_name] = dummies[x]\n",
    "    df.drop(name, axis=1, inplace=True)\n",
    "\n",
    "\n",
    "# Encode text values to indexes(i.e. [1],[2],[3] for red,green,blue).\n",
    "def encode_text_index(df, name):\n",
    "    le = preprocessing.LabelEncoder()\n",
    "    df[name] = le.fit_transform(df[name])\n",
    "    return le.classes_\n",
    "\n",
    "\n",
    "# Encode a numeric column as zscores\n",
    "def encode_numeric_zscore(df, name, mean=None, sd=None):\n",
    "    if mean is None:\n",
    "        mean = df[name].mean()\n",
    "\n",
    "    if sd is None:\n",
    "        sd = df[name].std()\n",
    "\n",
    "    df[name] = (df[name] - mean) / sd\n",
    "\n",
    "\n",
    "# Convert all missing values in the specified column to the median\n",
    "def missing_median(df, name):\n",
    "    med = df[name].median()\n",
    "    df[name] = df[name].fillna(med)\n",
    "\n",
    "\n",
    "# Convert all missing values in the specified column to the default\n",
    "def missing_default(df, name, default_value):\n",
    "    df[name] = df[name].fillna(default_value)\n",
    "\n",
    "\n",
    "# Convert a Pandas dataframe to the x,y inputs that TensorFlow needs\n",
    "def to_xy(df, target):\n",
    "    result = []\n",
    "    for x in df.columns:\n",
    "        if x != target:\n",
    "            result.append(x)\n",
    "    # find out the type of the target column. \n",
    "    target_type = df[target].dtypes\n",
    "    target_type = target_type[0] if isinstance(target_type, collections.Sequence) else target_type\n",
    "    # Encode to int for classification, float otherwise. TensorFlow likes 32 bits.\n",
    "    if target_type in (np.int64, np.int32):\n",
    "        # Classification\n",
    "        dummies = pd.get_dummies(df[target])\n",
    "        return df[result].values.astype(np.float32), dummies.values.astype(np.float32)\n",
    "    else:\n",
    "        # Regression\n",
    "        return df[result].values.astype(np.float32), df[target].values.astype(np.float32)\n",
    "\n",
    "# Nicely formatted time string\n",
    "def hms_string(sec_elapsed):\n",
    "    h = int(sec_elapsed / (60 * 60))\n",
    "    m = int((sec_elapsed % (60 * 60)) / 60)\n",
    "    s = sec_elapsed % 60\n",
    "    return \"{}:{:>02}:{:>05.2f}\".format(h, m, s)\n",
    "\n",
    "\n",
    "# Regression chart.\n",
    "def chart_regression(pred,y,sort=True):\n",
    "    t = pd.DataFrame({'pred' : pred, 'y' : y.flatten()})\n",
    "    if sort:\n",
    "        t.sort_values(by=['y'],inplace=True)\n",
    "    a = plt.plot(t['y'].tolist(),label='expected')\n",
    "    b = plt.plot(t['pred'].tolist(),label='prediction')\n",
    "    plt.ylabel('output')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Remove all rows where the specified column is +/- sd standard deviations\n",
    "def remove_outliers(df, name, sd):\n",
    "    drop_rows = df.index[(np.abs(df[name] - df[name].mean()) >= (sd * df[name].std()))]\n",
    "    df.drop(drop_rows, axis=0, inplace=True)\n",
    "\n",
    "\n",
    "# Encode a column to a range between normalized_low and normalized_high.\n",
    "def encode_numeric_range(df, name, normalized_low=-1, normalized_high=1,\n",
    "                         data_low=None, data_high=None):\n",
    "    if data_low is None:\n",
    "        data_low = min(df[name])\n",
    "        data_high = max(df[name])\n",
    "\n",
    "    df[name] = ((df[name] - data_low) / (data_high - data_low)) \\\n",
    "               * (normalized_high - normalized_low) + normalized_low\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saumya Shah\n",
    "#Jait Purohit\n",
    "\n",
    "import sklearn.feature_extraction.text as sk_text\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from pandas import Series\n",
    "%matplotlib inline\n",
    "#reading json file into tabular format\n",
    "import json\n",
    "import csv\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename_read = 'network_intrusion_data.csv'\n",
    "df = pd.read_csv(filename_read, na_values=['NA','?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [\n",
    "'duration',\n",
    "'protocol_type',\n",
    "'service',\n",
    "'flag',\n",
    "'src_bytes',\n",
    "'dst_bytes',\n",
    "'land',\n",
    "'wrong_fragment',\n",
    "'urgent',\n",
    "'hot',\n",
    "'num_failed_logins',\n",
    "'logged_in',\n",
    "'num_compromised',\n",
    "'root_shell',\n",
    "'su_attempted',\n",
    "'num_root',\n",
    "'num_file_creations',\n",
    "'num_shells',\n",
    "'num_access_files',\n",
    "'num_outbound_cmds',\n",
    "'is_host_login',\n",
    "'is_guest_login',\n",
    "'count',\n",
    "'srv_count',\n",
    "'serror_rate',\n",
    "'srv_serror_rate',\n",
    "'rerror_rate',\n",
    "'srv_rerror_rate',\n",
    "'same_srv_rate',\n",
    "'diff_srv_rate',\n",
    "'srv_diff_host_rate',\n",
    "'dst_host_count',\n",
    "'dst_host_srv_count',\n",
    "'dst_host_same_srv_rate',\n",
    "'dst_host_diff_srv_rate',\n",
    "'dst_host_same_src_port_rate',\n",
    "'dst_host_srv_diff_host_rate',\n",
    "'dst_host_serror_rate',\n",
    "'dst_host_srv_serror_rate',\n",
    "'dst_host_rerror_rate',\n",
    "'dst_host_srv_rerror_rate',\n",
    "'outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>239</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>219</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp    http   SF        239        486     0   \n",
       "1         0           tcp    http   SF        235       1337     0   \n",
       "2         0           tcp    http   SF        219       1337     0   \n",
       "3         0           tcp    http   SF        217       2032     0   \n",
       "4         0           tcp    http   SF        217       2032     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot   ...     dst_host_srv_count  \\\n",
       "0               0       0    0   ...                     19   \n",
       "1               0       0    0   ...                     29   \n",
       "2               0       0    0   ...                     39   \n",
       "3               0       0    0   ...                     49   \n",
       "4               0       0    0   ...                     59   \n",
       "\n",
       "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                     1.0                     0.0   \n",
       "1                     1.0                     0.0   \n",
       "2                     1.0                     0.0   \n",
       "3                     1.0                     0.0   \n",
       "4                     1.0                     0.0   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.05                          0.0   \n",
       "1                         0.03                          0.0   \n",
       "2                         0.03                          0.0   \n",
       "3                         0.02                          0.0   \n",
       "4                         0.02                          0.0   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                   0.0                       0.0                   0.0   \n",
       "1                   0.0                       0.0                   0.0   \n",
       "2                   0.0                       0.0                   0.0   \n",
       "3                   0.0                       0.0                   0.0   \n",
       "4                   0.0                       0.0                   0.0   \n",
       "\n",
       "   dst_host_srv_rerror_rate  outcome  \n",
       "0                       0.0  normal.  \n",
       "1                       0.0  normal.  \n",
       "2                       0.0  normal.  \n",
       "3                       0.0  normal.  \n",
       "4                       0.0  normal.  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494020, 42)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Removing Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[df.columns[:-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of duplicates is 348437\n",
      "\n",
      "The total numer of rows is 494020\n"
     ]
    }
   ],
   "source": [
    "print('The number of duplicates is {}\\n'.format(X.duplicated().sum()))\n",
    "print('The total numer of rows is {}'.format(X.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>307</td>\n",
       "      <td>60990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2863</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>351</td>\n",
       "      <td>11485</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2976</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>506</td>\n",
       "      <td>2419</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3002</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>319</td>\n",
       "      <td>1374</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3129</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>234</td>\n",
       "      <td>1212</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3897</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>186</td>\n",
       "      <td>14064</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>309</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4549</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>312</td>\n",
       "      <td>9384</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4550</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>327</td>\n",
       "      <td>4141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4963</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>305</td>\n",
       "      <td>458</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>290</td>\n",
       "      <td>751</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5043</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>298</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5044</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>294</td>\n",
       "      <td>1380</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5045</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>284</td>\n",
       "      <td>2251</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5054</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>291</td>\n",
       "      <td>421</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5543</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>140</td>\n",
       "      <td>31614</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5545</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>205</td>\n",
       "      <td>13599</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>276</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5646</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>210</td>\n",
       "      <td>3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5771</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>185</td>\n",
       "      <td>306</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>220</td>\n",
       "      <td>398</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5835</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6088</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>347</td>\n",
       "      <td>3718</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6091</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>323</td>\n",
       "      <td>3675</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6238</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>231</td>\n",
       "      <td>2313</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7030</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>152</td>\n",
       "      <td>8159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7291</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>202</td>\n",
       "      <td>316</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7481</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>215</td>\n",
       "      <td>24860</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7819</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>1032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>smurf.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7820</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>1032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>smurf.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491383</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>private</td>\n",
       "      <td>SF</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>248</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491385</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>private</td>\n",
       "      <td>SF</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>249</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491419</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491420</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491421</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>27</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491422</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491423</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491424</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491425</th>\n",
       "      <td>0</td>\n",
       "      <td>icmp</td>\n",
       "      <td>ecr_i</td>\n",
       "      <td>SF</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>67</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491442</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>time</td>\n",
       "      <td>SF</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491521</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491533</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491555</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491595</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491597</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491616</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491620</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491621</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491622</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491633</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491634</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491931</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>289</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492656</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>516</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493019</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>205</td>\n",
       "      <td>659</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493028</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>284</td>\n",
       "      <td>659</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493141</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>303</td>\n",
       "      <td>480</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493167</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>230</td>\n",
       "      <td>43464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493324</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>337</td>\n",
       "      <td>398</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493749</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>206</td>\n",
       "      <td>998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493750</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>221</td>\n",
       "      <td>1158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>348435 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration protocol_type  service flag  src_bytes  dst_bytes  land  \\\n",
       "2499           0           tcp     http   SF        307      60990     0   \n",
       "2863           0           tcp     http   SF        351      11485     0   \n",
       "2976           0           tcp     http   SF        506       2419     0   \n",
       "3002           0           tcp     http   SF        319       1374     0   \n",
       "3129           0           tcp     http   SF        234       1212     0   \n",
       "3897           0           tcp     http   SF        186      14064     0   \n",
       "3930           0           tcp     http   SF        309        349     0   \n",
       "4549           0           tcp     http   SF        312       9384     0   \n",
       "4550           0           tcp     http   SF        327       4141     0   \n",
       "4963           0           tcp     http   SF        305        458     0   \n",
       "5042           0           tcp     http   SF        290        751     0   \n",
       "5043           0           tcp     http   SF        298        626     0   \n",
       "5044           0           tcp     http   SF        294       1380     0   \n",
       "5045           0           tcp     http   SF        284       2251     0   \n",
       "5054           0           tcp     http   SF        291        421     0   \n",
       "5543           0           tcp     http   SF        140      31614     0   \n",
       "5545           0           tcp     http   SF        205      13599     0   \n",
       "5571           0           tcp     http   SF        276        365     0   \n",
       "5646           0           tcp     http   SF        210       3200     0   \n",
       "5771           0           tcp     http   SF        185        306     0   \n",
       "5780           0           tcp     http   SF        220        398     0   \n",
       "5835           0           tcp     http   SF        235        370     0   \n",
       "6088           0           tcp     http   SF        347       3718     0   \n",
       "6091           0           tcp     http   SF        323       3675     0   \n",
       "6238           0           tcp     http   SF        231       2313     0   \n",
       "7030           0           tcp     http   SF        152       8159     0   \n",
       "7291           0           tcp     http   SF        202        316     0   \n",
       "7481           0           tcp     http   SF        215      24860     0   \n",
       "7819           0          icmp    ecr_i   SF       1032          0     0   \n",
       "7820           0          icmp    ecr_i   SF       1032          0     0   \n",
       "...          ...           ...      ...  ...        ...        ...   ...   \n",
       "491383         0           udp  private   SF        105          0     0   \n",
       "491385         0           udp  private   SF        105          0     0   \n",
       "491419         0          icmp    ecr_i   SF         30          0     0   \n",
       "491420         0          icmp    ecr_i   SF         30          0     0   \n",
       "491421         0          icmp    ecr_i   SF         30          0     0   \n",
       "491422         0          icmp    ecr_i   SF         30          0     0   \n",
       "491423         0          icmp    ecr_i   SF         30          0     0   \n",
       "491424         0          icmp    ecr_i   SF         30          0     0   \n",
       "491425         0          icmp    ecr_i   SF         30          0     0   \n",
       "491442         0           tcp     time   SF          0          4     0   \n",
       "491521         0           udp    other   SF        147          0     0   \n",
       "491533         0           udp    other   SF        147          0     0   \n",
       "491555         0           udp    other   SF        147          0     0   \n",
       "491595         0           udp    other   SF        147          0     0   \n",
       "491597         0           udp    other   SF        147          0     0   \n",
       "491616         0           udp    other   SF        147        147     0   \n",
       "491620         0           udp    other   SF        147        147     0   \n",
       "491621         0           udp    other   SF        147        147     0   \n",
       "491622         0           udp    other   SF        147        147     0   \n",
       "491633         0           udp    other   SF        147          0     0   \n",
       "491634         0           udp    other   SF        147          0     0   \n",
       "491931         0           tcp     http   SF        289        280     0   \n",
       "492656         0           udp    other   SF        516          4     0   \n",
       "493019         0           tcp     http   SF        205        659     0   \n",
       "493028         0           tcp     http   SF        284        659     0   \n",
       "493141         0           tcp     http   SF        303        480     0   \n",
       "493167         0           tcp     http   SF        230      43464     0   \n",
       "493324         0           tcp     http   SF        337        398     0   \n",
       "493749         0           tcp     http   SF        206        998     0   \n",
       "493750         0           tcp     http   SF        221       1158     0   \n",
       "\n",
       "        wrong_fragment  urgent  hot   ...     dst_host_srv_count  \\\n",
       "2499                 0       0    0   ...                    255   \n",
       "2863                 0       0    0   ...                    255   \n",
       "2976                 0       0    0   ...                    255   \n",
       "3002                 0       0    0   ...                    255   \n",
       "3129                 0       0    0   ...                    255   \n",
       "3897                 0       0    0   ...                    255   \n",
       "3930                 0       0    0   ...                    255   \n",
       "4549                 0       0    0   ...                    255   \n",
       "4550                 0       0    0   ...                    255   \n",
       "4963                 0       0    0   ...                    255   \n",
       "5042                 0       0    0   ...                    255   \n",
       "5043                 0       0    0   ...                    255   \n",
       "5044                 0       0    0   ...                    255   \n",
       "5045                 0       0    0   ...                    255   \n",
       "5054                 0       0    0   ...                    255   \n",
       "5543                 0       0    0   ...                    255   \n",
       "5545                 0       0    0   ...                    255   \n",
       "5571                 0       0    0   ...                    255   \n",
       "5646                 0       0    0   ...                    255   \n",
       "5771                 0       0    0   ...                    255   \n",
       "5780                 0       0    0   ...                    255   \n",
       "5835                 0       0    0   ...                    255   \n",
       "6088                 0       0    0   ...                    255   \n",
       "6091                 0       0    0   ...                    255   \n",
       "6238                 0       0    0   ...                    255   \n",
       "7030                 0       0    0   ...                    255   \n",
       "7291                 0       0    0   ...                    255   \n",
       "7481                 0       0    0   ...                    255   \n",
       "7819                 0       0    0   ...                    255   \n",
       "7820                 0       0    0   ...                    255   \n",
       "...                ...     ...  ...   ...                    ...   \n",
       "491383               0       0    0   ...                    248   \n",
       "491385               0       0    0   ...                    249   \n",
       "491419               0       0    0   ...                      7   \n",
       "491420               0       0    0   ...                     17   \n",
       "491421               0       0    0   ...                     27   \n",
       "491422               0       0    0   ...                     37   \n",
       "491423               0       0    0   ...                     47   \n",
       "491424               0       0    0   ...                     57   \n",
       "491425               0       0    0   ...                     67   \n",
       "491442               0       0    0   ...                     22   \n",
       "491521               0       0    0   ...                      1   \n",
       "491533               0       0    0   ...                      1   \n",
       "491555               0       0    0   ...                      1   \n",
       "491595               0       0    0   ...                      1   \n",
       "491597               0       0    0   ...                      1   \n",
       "491616               0       0    0   ...                      1   \n",
       "491620               0       0    0   ...                      1   \n",
       "491621               0       0    0   ...                      1   \n",
       "491622               0       0    0   ...                      1   \n",
       "491633               0       0    0   ...                      1   \n",
       "491634               0       0    0   ...                      1   \n",
       "491931               0       0    0   ...                    255   \n",
       "492656               0       0    0   ...                      8   \n",
       "493019               0       0    0   ...                    255   \n",
       "493028               0       0    0   ...                    255   \n",
       "493141               0       0    0   ...                    255   \n",
       "493167               0       0    0   ...                    255   \n",
       "493324               0       0    0   ...                    255   \n",
       "493749               0       0    0   ...                    255   \n",
       "493750               0       0    0   ...                    255   \n",
       "\n",
       "        dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "2499                      1.00                    0.00   \n",
       "2863                      1.00                    0.00   \n",
       "2976                      1.00                    0.00   \n",
       "3002                      1.00                    0.00   \n",
       "3129                      1.00                    0.00   \n",
       "3897                      1.00                    0.00   \n",
       "3930                      1.00                    0.00   \n",
       "4549                      1.00                    0.00   \n",
       "4550                      1.00                    0.00   \n",
       "4963                      1.00                    0.00   \n",
       "5042                      1.00                    0.00   \n",
       "5043                      1.00                    0.00   \n",
       "5044                      1.00                    0.00   \n",
       "5045                      1.00                    0.00   \n",
       "5054                      1.00                    0.00   \n",
       "5543                      1.00                    0.00   \n",
       "5545                      1.00                    0.00   \n",
       "5571                      1.00                    0.00   \n",
       "5646                      1.00                    0.00   \n",
       "5771                      1.00                    0.00   \n",
       "5780                      1.00                    0.00   \n",
       "5835                      1.00                    0.00   \n",
       "6088                      1.00                    0.00   \n",
       "6091                      1.00                    0.00   \n",
       "6238                      1.00                    0.00   \n",
       "7030                      1.00                    0.00   \n",
       "7291                      1.00                    0.00   \n",
       "7481                      1.00                    0.00   \n",
       "7819                      1.00                    0.00   \n",
       "7820                      1.00                    0.00   \n",
       "...                        ...                     ...   \n",
       "491383                    0.97                    0.01   \n",
       "491385                    0.98                    0.01   \n",
       "491419                    1.00                    0.00   \n",
       "491420                    1.00                    0.00   \n",
       "491421                    1.00                    0.00   \n",
       "491422                    1.00                    0.00   \n",
       "491423                    1.00                    0.00   \n",
       "491424                    1.00                    0.00   \n",
       "491425                    1.00                    0.00   \n",
       "491442                    1.00                    0.00   \n",
       "491521                    0.00                    0.43   \n",
       "491533                    0.00                    0.44   \n",
       "491555                    0.00                    0.44   \n",
       "491595                    0.00                    0.59   \n",
       "491597                    0.00                    0.60   \n",
       "491616                    0.00                    0.69   \n",
       "491620                    0.00                    0.70   \n",
       "491621                    0.00                    0.70   \n",
       "491622                    0.00                    0.70   \n",
       "491633                    0.00                    0.71   \n",
       "491634                    0.00                    0.71   \n",
       "491931                    1.00                    0.00   \n",
       "492656                    1.00                    0.00   \n",
       "493019                    1.00                    0.00   \n",
       "493028                    1.00                    0.00   \n",
       "493141                    1.00                    0.00   \n",
       "493167                    1.00                    0.00   \n",
       "493324                    1.00                    0.00   \n",
       "493749                    1.00                    0.00   \n",
       "493750                    1.00                    0.00   \n",
       "\n",
       "        dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "2499                           0.00                         0.00   \n",
       "2863                           0.00                         0.00   \n",
       "2976                           0.00                         0.00   \n",
       "3002                           0.00                         0.00   \n",
       "3129                           0.00                         0.00   \n",
       "3897                           0.00                         0.00   \n",
       "3930                           0.00                         0.00   \n",
       "4549                           0.06                         0.02   \n",
       "4550                           0.04                         0.02   \n",
       "4963                           0.00                         0.00   \n",
       "5042                           0.00                         0.00   \n",
       "5043                           0.00                         0.00   \n",
       "5044                           0.00                         0.00   \n",
       "5045                           0.00                         0.00   \n",
       "5054                           0.00                         0.00   \n",
       "5543                           0.00                         0.00   \n",
       "5545                           0.00                         0.00   \n",
       "5571                           0.00                         0.00   \n",
       "5646                           0.00                         0.00   \n",
       "5771                           0.00                         0.00   \n",
       "5780                           0.00                         0.00   \n",
       "5835                           0.00                         0.00   \n",
       "6088                           0.00                         0.00   \n",
       "6091                           0.00                         0.00   \n",
       "6238                           0.00                         0.00   \n",
       "7030                           0.00                         0.00   \n",
       "7291                           0.00                         0.00   \n",
       "7481                           0.00                         0.00   \n",
       "7819                           1.00                         0.00   \n",
       "7820                           1.00                         0.00   \n",
       "...                             ...                          ...   \n",
       "491383                         0.01                         0.00   \n",
       "491385                         0.01                         0.00   \n",
       "491419                         1.00                         0.00   \n",
       "491420                         1.00                         0.00   \n",
       "491421                         1.00                         0.00   \n",
       "491422                         1.00                         0.00   \n",
       "491423                         1.00                         0.00   \n",
       "491424                         1.00                         0.00   \n",
       "491425                         1.00                         0.00   \n",
       "491442                         0.05                         0.00   \n",
       "491521                         0.93                         0.00   \n",
       "491533                         0.93                         0.00   \n",
       "491555                         0.84                         0.00   \n",
       "491595                         0.98                         0.00   \n",
       "491597                         0.98                         0.00   \n",
       "491616                         0.98                         0.00   \n",
       "491620                         0.98                         0.00   \n",
       "491621                         0.98                         0.00   \n",
       "491622                         0.98                         0.00   \n",
       "491633                         0.97                         0.00   \n",
       "491634                         0.95                         0.00   \n",
       "491931                         0.00                         0.00   \n",
       "492656                         1.00                         0.00   \n",
       "493019                         0.00                         0.00   \n",
       "493028                         0.00                         0.00   \n",
       "493141                         0.00                         0.00   \n",
       "493167                         0.00                         0.00   \n",
       "493324                         0.00                         0.00   \n",
       "493749                         0.00                         0.00   \n",
       "493750                         0.00                         0.00   \n",
       "\n",
       "        dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "2499                     0.0                       0.0                   0.0   \n",
       "2863                     0.0                       0.0                   0.0   \n",
       "2976                     0.0                       0.0                   0.0   \n",
       "3002                     0.0                       0.0                   0.0   \n",
       "3129                     0.0                       0.0                   0.0   \n",
       "3897                     0.0                       0.0                   0.0   \n",
       "3930                     0.0                       0.0                   0.0   \n",
       "4549                     0.0                       0.0                   0.0   \n",
       "4550                     0.0                       0.0                   0.0   \n",
       "4963                     0.0                       0.0                   0.0   \n",
       "5042                     0.0                       0.0                   0.0   \n",
       "5043                     0.0                       0.0                   0.0   \n",
       "5044                     0.0                       0.0                   0.0   \n",
       "5045                     0.0                       0.0                   0.0   \n",
       "5054                     0.0                       0.0                   0.0   \n",
       "5543                     0.0                       0.0                   0.0   \n",
       "5545                     0.0                       0.0                   0.0   \n",
       "5571                     0.0                       0.0                   0.0   \n",
       "5646                     0.0                       0.0                   0.0   \n",
       "5771                     0.0                       0.0                   0.0   \n",
       "5780                     0.0                       0.0                   0.0   \n",
       "5835                     0.0                       0.0                   0.0   \n",
       "6088                     0.0                       0.0                   0.0   \n",
       "6091                     0.0                       0.0                   0.0   \n",
       "6238                     0.0                       0.0                   0.0   \n",
       "7030                     0.0                       0.0                   0.0   \n",
       "7291                     0.0                       0.0                   0.0   \n",
       "7481                     0.0                       0.0                   0.0   \n",
       "7819                     0.0                       0.0                   0.0   \n",
       "7820                     0.0                       0.0                   0.0   \n",
       "...                      ...                       ...                   ...   \n",
       "491383                   0.0                       0.0                   0.0   \n",
       "491385                   0.0                       0.0                   0.0   \n",
       "491419                   0.0                       0.0                   0.0   \n",
       "491420                   0.0                       0.0                   0.0   \n",
       "491421                   0.0                       0.0                   0.0   \n",
       "491422                   0.0                       0.0                   0.0   \n",
       "491423                   0.0                       0.0                   0.0   \n",
       "491424                   0.0                       0.0                   0.0   \n",
       "491425                   0.0                       0.0                   0.0   \n",
       "491442                   0.0                       0.0                   0.0   \n",
       "491521                   0.0                       0.0                   0.0   \n",
       "491533                   0.0                       0.0                   0.0   \n",
       "491555                   0.0                       0.0                   0.0   \n",
       "491595                   0.0                       0.0                   0.0   \n",
       "491597                   0.0                       0.0                   0.0   \n",
       "491616                   0.0                       0.0                   0.0   \n",
       "491620                   0.0                       0.0                   0.0   \n",
       "491621                   0.0                       0.0                   0.0   \n",
       "491622                   0.0                       0.0                   0.0   \n",
       "491633                   0.0                       0.0                   0.0   \n",
       "491634                   0.0                       0.0                   0.0   \n",
       "491931                   0.0                       0.0                   0.0   \n",
       "492656                   0.0                       0.0                   0.0   \n",
       "493019                   0.0                       0.0                   0.0   \n",
       "493028                   0.0                       0.0                   0.0   \n",
       "493141                   0.0                       0.0                   0.0   \n",
       "493167                   0.0                       0.0                   0.0   \n",
       "493324                   0.0                       0.0                   0.0   \n",
       "493749                   0.0                       0.0                   0.0   \n",
       "493750                   0.0                       0.0                   0.0   \n",
       "\n",
       "        dst_host_srv_rerror_rate  outcome  \n",
       "2499                         0.0  normal.  \n",
       "2863                         0.0  normal.  \n",
       "2976                         0.0  normal.  \n",
       "3002                         0.0  normal.  \n",
       "3129                         0.0  normal.  \n",
       "3897                         0.0  normal.  \n",
       "3930                         0.0  normal.  \n",
       "4549                         0.0  normal.  \n",
       "4550                         0.0  normal.  \n",
       "4963                         0.0  normal.  \n",
       "5042                         0.0  normal.  \n",
       "5043                         0.0  normal.  \n",
       "5044                         0.0  normal.  \n",
       "5045                         0.0  normal.  \n",
       "5054                         0.0  normal.  \n",
       "5543                         0.0  normal.  \n",
       "5545                         0.0  normal.  \n",
       "5571                         0.0  normal.  \n",
       "5646                         0.0  normal.  \n",
       "5771                         0.0  normal.  \n",
       "5780                         0.0  normal.  \n",
       "5835                         0.0  normal.  \n",
       "6088                         0.0  normal.  \n",
       "6091                         0.0  normal.  \n",
       "6238                         0.0  normal.  \n",
       "7030                         0.0  normal.  \n",
       "7291                         0.0  normal.  \n",
       "7481                         0.0  normal.  \n",
       "7819                         0.0   smurf.  \n",
       "7820                         0.0   smurf.  \n",
       "...                          ...      ...  \n",
       "491383                       0.0  normal.  \n",
       "491385                       0.0  normal.  \n",
       "491419                       0.0  normal.  \n",
       "491420                       0.0  normal.  \n",
       "491421                       0.0  normal.  \n",
       "491422                       0.0  normal.  \n",
       "491423                       0.0  normal.  \n",
       "491424                       0.0  normal.  \n",
       "491425                       0.0  normal.  \n",
       "491442                       0.0  normal.  \n",
       "491521                       0.0  normal.  \n",
       "491533                       0.0  normal.  \n",
       "491555                       0.0  normal.  \n",
       "491595                       0.0  normal.  \n",
       "491597                       0.0  normal.  \n",
       "491616                       0.0  normal.  \n",
       "491620                       0.0  normal.  \n",
       "491621                       0.0  normal.  \n",
       "491622                       0.0  normal.  \n",
       "491633                       0.0  normal.  \n",
       "491634                       0.0  normal.  \n",
       "491931                       0.0  normal.  \n",
       "492656                       0.0  normal.  \n",
       "493019                       0.0  normal.  \n",
       "493028                       0.0  normal.  \n",
       "493141                       0.0  normal.  \n",
       "493167                       0.0  normal.  \n",
       "493324                       0.0  normal.  \n",
       "493749                       0.0  normal.  \n",
       "493750                       0.0  normal.  \n",
       "\n",
       "[348435 rows x 42 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.duplicated(), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows and columns: (494020, 42)\n",
      "rows and columns after removing duplicates:(145585, 42)\n"
     ]
    }
   ],
   "source": [
    "print('rows and columns: ' + str(df.shape))\n",
    "df = df.drop_duplicates()\n",
    "print('rows and columns after removing duplicates:' + str(df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.isnull().any(axis=1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration   2495\n",
      "protocol_type   3\n",
      "service   66\n",
      "flag   11\n",
      "src_bytes   3300\n",
      "dst_bytes   10725\n",
      "land   2\n",
      "wrong_fragment   3\n",
      "urgent   4\n",
      "hot   22\n",
      "num_failed_logins   6\n",
      "logged_in   2\n",
      "num_compromised   23\n",
      "root_shell   2\n",
      "su_attempted   3\n",
      "num_root   20\n",
      "num_file_creations   18\n",
      "num_shells   3\n",
      "num_access_files   7\n",
      "num_outbound_cmds   1\n",
      "is_host_login   1\n",
      "is_guest_login   2\n",
      "count   490\n",
      "srv_count   470\n",
      "serror_rate   92\n",
      "srv_serror_rate   51\n",
      "rerror_rate   77\n",
      "srv_rerror_rate   51\n",
      "same_srv_rate   99\n",
      "diff_srv_rate   78\n",
      "srv_diff_host_rate   64\n",
      "dst_host_count   256\n",
      "dst_host_srv_count   256\n",
      "dst_host_same_srv_rate   101\n",
      "dst_host_diff_srv_rate   101\n",
      "dst_host_same_src_port_rate   101\n",
      "dst_host_srv_diff_host_rate   65\n",
      "dst_host_serror_rate   100\n",
      "dst_host_srv_serror_rate   72\n",
      "dst_host_rerror_rate   101\n",
      "dst_host_srv_rerror_rate   101\n",
      "outcome   23\n"
     ]
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    print(i,\" \",len(df[i].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Categorizing outcome attacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def labels_map(outcome):\n",
    "    \"\"\"\n",
    "     0 - normal\n",
    "     1 - probing -  surveillance and other probing, e.g., port scanning.\n",
    "     2 - DOS - denial-of-service, e.g. syn flood;\n",
    "     3 - U2R - unauthorized access to local superuser (root) privileges, e.g., various __buffer overflow__ attacks;\n",
    "     4 - R2L - unauthorized access from a remote machine, e.g. guessing password;\n",
    "    \"\"\"\n",
    "    outcome = str(outcome).split('.')[0]\n",
    "    if outcome == 'normal':\n",
    "        return 0\n",
    "    if outcome in ['ipsweep', 'mscan', 'nmap', 'portsweep', 'saint', 'satan']: \n",
    "        return 1\n",
    "    if outcome in ['apache2', 'back', 'land', 'mailbomb', 'neptune', 'pod', 'processtable', 'smurf', 'teardrop', 'udpstorm']:\n",
    "        return 2\n",
    "    if outcome in ['buffer_overflow', 'httptunnel', 'loadmodule', 'perl', 'ps', 'rootkit', 'sqlattack', 'xterm']: \n",
    "        return 3\n",
    "    if outcome in ['ftp_write', 'guess_passwd', 'imap', 'multihop', 'named', 'phf', 'sendmail', 'snmpgetattack',\n",
    "                 'snmpguess', 'spy', 'warezclient', 'warezmaster', 'worm', 'xlock', 'xsnoop']: \n",
    "        return 4\n",
    "df['enc_outcome'] = df.outcome.apply(labels_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    87831\n",
       "2    54572\n",
       "1     2131\n",
       "4      999\n",
       "3       52\n",
       "Name: enc_outcome, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.enc_outcome.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "enc_outcome\n",
       "0    60.329704\n",
       "1     1.463750\n",
       "2    37.484631\n",
       "3     0.035718\n",
       "4     0.686197\n",
       "dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows_count = df.shape[0]\n",
    "df.groupby('enc_outcome').size() * 100/rows_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>outcome</th>\n",
       "      <th>enc_outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>239</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>219</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp    http   SF        239        486     0   \n",
       "1         0           tcp    http   SF        235       1337     0   \n",
       "2         0           tcp    http   SF        219       1337     0   \n",
       "3         0           tcp    http   SF        217       2032     0   \n",
       "4         0           tcp    http   SF        217       2032     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot     ...       dst_host_same_srv_rate  \\\n",
       "0               0       0    0     ...                          1.0   \n",
       "1               0       0    0     ...                          1.0   \n",
       "2               0       0    0     ...                          1.0   \n",
       "3               0       0    0     ...                          1.0   \n",
       "4               0       0    0     ...                          1.0   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                     0.0                         0.05   \n",
       "1                     0.0                         0.03   \n",
       "2                     0.0                         0.03   \n",
       "3                     0.0                         0.02   \n",
       "4                     0.0                         0.02   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "0                          0.0                   0.0   \n",
       "1                          0.0                   0.0   \n",
       "2                          0.0                   0.0   \n",
       "3                          0.0                   0.0   \n",
       "4                          0.0                   0.0   \n",
       "\n",
       "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
       "0                       0.0                   0.0                       0.0   \n",
       "1                       0.0                   0.0                       0.0   \n",
       "2                       0.0                   0.0                       0.0   \n",
       "3                       0.0                   0.0                       0.0   \n",
       "4                       0.0                   0.0                       0.0   \n",
       "\n",
       "   outcome  enc_outcome  \n",
       "0  normal.            0  \n",
       "1  normal.            0  \n",
       "2  normal.            0  \n",
       "3  normal.            0  \n",
       "4  normal.            0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehotcols = ['protocol_type','service','flag','land','logged_in','root_shell','is_host_login','is_guest_login',]\n",
    "for i in onehotcols:\n",
    "    encode_text_dummy(df,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145585, 124)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duration   2495\n",
      "src_bytes   3300\n",
      "dst_bytes   10725\n",
      "hot   22\n",
      "num_compromised   23\n",
      "num_root   20\n",
      "num_file_creations   18\n",
      "count   490\n",
      "srv_count   470\n",
      "serror_rate   92\n",
      "srv_serror_rate   51\n",
      "rerror_rate   77\n",
      "srv_rerror_rate   51\n",
      "same_srv_rate   99\n",
      "diff_srv_rate   78\n",
      "srv_diff_host_rate   64\n",
      "dst_host_count   256\n",
      "dst_host_srv_count   256\n",
      "dst_host_same_srv_rate   101\n",
      "dst_host_diff_srv_rate   101\n",
      "dst_host_same_src_port_rate   101\n",
      "dst_host_srv_diff_host_rate   65\n",
      "dst_host_serror_rate   100\n",
      "dst_host_srv_serror_rate   72\n",
      "dst_host_rerror_rate   101\n",
      "dst_host_srv_rerror_rate   101\n"
     ]
    }
   ],
   "source": [
    "zscorecols = [\n",
    "'duration',\n",
    "'src_bytes',\n",
    "'dst_bytes',\n",
    "'hot',\n",
    "'num_compromised',\n",
    "'num_root',\n",
    "'num_file_creations',\n",
    "'count',\n",
    "'srv_count',\n",
    "'serror_rate',\n",
    "'srv_serror_rate',\n",
    "'rerror_rate',\n",
    "'srv_rerror_rate',\n",
    "'same_srv_rate',\n",
    "'diff_srv_rate',\n",
    "'srv_diff_host_rate',\n",
    "'dst_host_count',\n",
    "'dst_host_srv_count',\n",
    "'dst_host_same_srv_rate',\n",
    "'dst_host_diff_srv_rate',\n",
    "'dst_host_same_src_port_rate',\n",
    "'dst_host_srv_diff_host_rate',\n",
    "'dst_host_serror_rate',\n",
    "'dst_host_srv_serror_rate',\n",
    "'dst_host_rerror_rate',\n",
    "'dst_host_srv_rerror_rate']\n",
    "for i in zscorecols:\n",
    "    print(i,\" \",len(df[i].unique()))\n",
    "\n",
    "for i in zscorecols:\n",
    "    encode_numeric_zscore(df_new,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>duration</th>\n",
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       "<p>5 rows × 124 columns</p>\n",
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      "text/plain": [
       "   duration  src_bytes  dst_bytes  wrong_fragment  urgent      hot  \\\n",
       "0  -0.10785  -0.004261  -0.039036               0       0 -0.07021   \n",
       "1  -0.10785  -0.004263  -0.025041               0       0 -0.07021   \n",
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       "4  -0.10785  -0.004273  -0.013612               0       0 -0.07021   \n",
       "\n",
       "   num_failed_logins  num_compromised  su_attempted  num_root  \\\n",
       "0                  0        -0.007905             0  -0.01039   \n",
       "1                  0        -0.007905             0  -0.01039   \n",
       "2                  0        -0.007905             0  -0.01039   \n",
       "3                  0        -0.007905             0  -0.01039   \n",
       "4                  0        -0.007905             0  -0.01039   \n",
       "\n",
       "         ...         flag-SH  land-0  land-1  logged_in-0  logged_in-1  \\\n",
       "0        ...               0       1       0            0            1   \n",
       "1        ...               0       1       0            0            1   \n",
       "2        ...               0       1       0            0            1   \n",
       "3        ...               0       1       0            0            1   \n",
       "4        ...               0       1       0            0            1   \n",
       "\n",
       "   root_shell-0  root_shell-1  is_host_login-0  is_guest_login-0  \\\n",
       "0             1             0                1                 1   \n",
       "1             1             0                1                 1   \n",
       "2             1             0                1                 1   \n",
       "3             1             0                1                 1   \n",
       "4             1             0                1                 1   \n",
       "\n",
       "   is_guest_login-1  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 2, 4, 1])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['enc_outcome'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145585, 124)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.drop(columns='outcome',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "traincols = df_new.columns.tolist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "traincols.remove('enc_outcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  src_bytes  dst_bytes  wrong_fragment  urgent      hot  \\\n",
       "0  -0.10785  -0.004261  -0.039036               0       0 -0.07021   \n",
       "1  -0.10785  -0.004263  -0.025041               0       0 -0.07021   \n",
       "2  -0.10785  -0.004272  -0.025041               0       0 -0.07021   \n",
       "3  -0.10785  -0.004273  -0.013612               0       0 -0.07021   \n",
       "4  -0.10785  -0.004273  -0.013612               0       0 -0.07021   \n",
       "\n",
       "   num_failed_logins  num_compromised  su_attempted  num_root  \\\n",
       "0                  0        -0.007905             0  -0.01039   \n",
       "1                  0        -0.007905             0  -0.01039   \n",
       "2                  0        -0.007905             0  -0.01039   \n",
       "3                  0        -0.007905             0  -0.01039   \n",
       "4                  0        -0.007905             0  -0.01039   \n",
       "\n",
       "         ...         flag-SH  land-0  land-1  logged_in-0  logged_in-1  \\\n",
       "0        ...               0       1       0            0            1   \n",
       "1        ...               0       1       0            0            1   \n",
       "2        ...               0       1       0            0            1   \n",
       "3        ...               0       1       0            0            1   \n",
       "4        ...               0       1       0            0            1   \n",
       "\n",
       "   root_shell-0  root_shell-1  is_host_login-0  is_guest_login-0  \\\n",
       "0             1             0                1                 1   \n",
       "1             1             0                1                 1   \n",
       "2             1             0                1                 1   \n",
       "3             1             0                1                 1   \n",
       "4             1             0                1                 1   \n",
       "\n",
       "   is_guest_login-1  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "\n",
       "[5 rows x 123 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_new[traincols],df_new['enc_outcome'], test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logistic Regression\n",
    "from sklearn import metrics\n",
    "logreg = LogisticRegression()\n",
    "# fit the model with data\n",
    "logreg.fit(x_train,y_train)\n",
    "# predict the response for new observations\n",
    "logisticprediction = logreg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9948827145653741\n",
      "Precision:  0.9949129899411444\n",
      "Recall:  0.9948827145653741\n",
      "F1_Score:  0.9948685815922546\n",
      "Confusion Matrix:  [[17550     5    13     0    21]\n",
      " [   21   397     6     0     1]\n",
      " [   61     1 10862     0     0]\n",
      " [    1     0     0     4     2]\n",
      " [   17     0     0     0   155]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: \", metrics.accuracy_score(y_test,logisticprediction))\n",
    "print(\"Precision: \", metrics.precision_score(y_test,logisticprediction, pos_label=1, average='weighted'))\n",
    "print(\"Recall: \", metrics.recall_score(y_test,logisticprediction, pos_label=1, average='weighted'))\n",
    "print(\"F1_Score: \", metrics.f1_score(y_test,logisticprediction, pos_label=1, average='weighted'))\n",
    "print(\"Confusion Matrix: \", metrics.confusion_matrix(y_test,logisticprediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, names, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(names))\n",
    "    plt.xticks(tick_marks, names, rotation=45)\n",
    "    plt.yticks(tick_marks, names)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "def plot_roc(pred,y):\n",
    "    fpr, tpr, thresholds = roc_curve(y, pred)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver Operating Characteristic (ROC)')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17550     5    13     0    21]\n",
      " [   21   397     6     0     1]\n",
      " [   61     1 10862     0     0]\n",
      " [    1     0     0     4     2]\n",
      " [   17     0     0     0   155]]\n",
      "Plotting confusion matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      1.00      1.00     17589\n",
      "          1       0.99      0.93      0.96       425\n",
      "          2       1.00      0.99      1.00     10924\n",
      "          3       1.00      0.57      0.73         7\n",
      "          4       0.87      0.90      0.88       172\n",
      "\n",
      "avg / total       0.99      0.99      0.99     29117\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "cm = confusion_matrix(y_test, logisticprediction)\n",
    "print(cm)\n",
    "outcome = encode_text_index(df_new,'enc_outcome')\n",
    "\n",
    "print('Plotting confusion matrix')\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cm, outcome)\n",
    "plt.show()\n",
    "#pred = logisticprediction[:,1]\n",
    "print(classification_report(y_test, logisticprediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Feature Importance Analysis for Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier(max_depth=25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x = df_new[df_new.columns[:-1]]\n",
    "y = df['enc_outcome']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_new[traincols],df_new['enc_outcome'], test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>22931</th>\n",
       "      <td>-0.10785</td>\n",
       "      <td>-0.004370</td>\n",
       "      <td>-0.045498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.07021</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.007905</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.01039</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13683</th>\n",
       "      <td>-0.10785</td>\n",
       "      <td>-0.004267</td>\n",
       "      <td>-0.039743</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.07021</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.007905</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.01039</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration  src_bytes  dst_bytes  wrong_fragment  urgent      hot  \\\n",
       "474750  -0.10785  -0.004392  -0.047028               0       0 -0.07021   \n",
       "382832  -0.10785  -0.004392  -0.047028               0       0 -0.07021   \n",
       "492858  -0.10785  -0.004276   0.140441               0       0 -0.07021   \n",
       "22931   -0.10785  -0.004370  -0.045498               0       0 -0.07021   \n",
       "13683   -0.10785  -0.004267  -0.039743               0       0 -0.07021   \n",
       "\n",
       "        num_failed_logins  num_compromised  su_attempted  num_root  \\\n",
       "474750                  0        -0.007905             0  -0.01039   \n",
       "382832                  0        -0.007905             0  -0.01039   \n",
       "492858                  0        -0.007905             0  -0.01039   \n",
       "22931                   0        -0.007905             0  -0.01039   \n",
       "13683                   0        -0.007905             0  -0.01039   \n",
       "\n",
       "              ...         flag-SH  land-0  land-1  logged_in-0  logged_in-1  \\\n",
       "474750        ...               0       1       0            1            0   \n",
       "382832        ...               0       1       0            1            0   \n",
       "492858        ...               0       1       0            0            1   \n",
       "22931         ...               0       1       0            1            0   \n",
       "13683         ...               0       1       0            0            1   \n",
       "\n",
       "        root_shell-0  root_shell-1  is_host_login-0  is_guest_login-0  \\\n",
       "474750             1             0                1                 1   \n",
       "382832             1             0                1                 1   \n",
       "492858             1             0                1                 1   \n",
       "22931              1             0                1                 1   \n",
       "13683              1             0                1                 1   \n",
       "\n",
       "        is_guest_login-1  \n",
       "474750                 0  \n",
       "382832                 0  \n",
       "492858                 0  \n",
       "22931                  0  \n",
       "13683                  0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "474750    2\n",
       "382832    2\n",
       "492858    0\n",
       "22931     0\n",
       "13683     0\n",
       "Name: enc_outcome, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "483593    0\n",
       "140915    1\n",
       "106991    0\n",
       "64590     2\n",
       "32992     0\n",
       "Name: enc_outcome, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=25, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['duration', 'src_bytes', 'dst_bytes', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'num_compromised', 'su_attempted', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'protocol_type-icmp', 'protocol_type-tcp', 'protocol_type-udp', 'service-IRC', 'service-X11', 'service-Z39_50', 'service-auth', 'service-bgp', 'service-courier', 'service-csnet_ns', 'service-ctf', 'service-daytime', 'service-discard', 'service-domain', 'service-domain_u', 'service-echo', 'service-eco_i', 'service-ecr_i', 'service-efs', 'service-exec', 'service-finger', 'service-ftp', 'service-ftp_data', 'service-gopher', 'service-hostnames', 'service-http', 'service-http_443', 'service-imap4', 'service-iso_tsap', 'service-klogin', 'service-kshell', 'service-ldap', 'service-link', 'service-login', 'service-mtp', 'service-name', 'service-netbios_dgm', 'service-netbios_ns', 'service-netbios_ssn', 'service-netstat', 'service-nnsp', 'service-nntp', 'service-ntp_u', 'service-other', 'service-pm_dump', 'service-pop_2', 'service-pop_3', 'service-printer', 'service-private', 'service-red_i', 'service-remote_job', 'service-rje', 'service-shell', 'service-smtp', 'service-sql_net', 'service-ssh', 'service-sunrpc', 'service-supdup', 'service-systat', 'service-telnet', 'service-tftp_u', 'service-tim_i', 'service-time', 'service-urh_i', 'service-urp_i', 'service-uucp', 'service-uucp_path', 'service-vmnet', 'service-whois', 'flag-OTH', 'flag-REJ', 'flag-RSTO', 'flag-RSTOS0', 'flag-RSTR', 'flag-S0', 'flag-S1', 'flag-S2', 'flag-S3', 'flag-SF', 'flag-SH', 'land-0', 'land-1', 'logged_in-0', 'logged_in-1', 'root_shell-0', 'root_shell-1', 'is_host_login-0', 'is_guest_login-0', 'is_guest_login-1']\n"
     ]
    }
   ],
   "source": [
    "importances=[]\n",
    "for (x,y) in zip((x_train.axes[1].values),rf.feature_importances_):\n",
    "    importances.append([x, y])\n",
    "    \n",
    "print([i[0] for i in importances])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a85b6a0>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(10,25))        \n",
    "sns.barplot(x=np.sort([i[1] for i in importances]),y=[i[0] for i in importances])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results of EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "We find many feature that doesn't impact on outcome-attacks\n",
    "Many samples had duplicate, so we have deleted it\n",
    "It seems that some of features are not important, so we can ignore it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>num_root</th>\n",
       "      <th>...</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.10785</td>\n",
       "      <td>-0.004273</td>\n",
       "      <td>-0.013612</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.07021</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.007905</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.01039</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  src_bytes  dst_bytes  wrong_fragment  urgent      hot  \\\n",
       "0  -0.10785  -0.004261  -0.039036               0       0 -0.07021   \n",
       "1  -0.10785  -0.004263  -0.025041               0       0 -0.07021   \n",
       "2  -0.10785  -0.004272  -0.025041               0       0 -0.07021   \n",
       "3  -0.10785  -0.004273  -0.013612               0       0 -0.07021   \n",
       "4  -0.10785  -0.004273  -0.013612               0       0 -0.07021   \n",
       "\n",
       "   num_failed_logins  num_compromised  su_attempted  num_root  \\\n",
       "0                  0        -0.007905             0  -0.01039   \n",
       "1                  0        -0.007905             0  -0.01039   \n",
       "2                  0        -0.007905             0  -0.01039   \n",
       "3                  0        -0.007905             0  -0.01039   \n",
       "4                  0        -0.007905             0  -0.01039   \n",
       "\n",
       "         ...         flag-SH  land-0  land-1  logged_in-0  logged_in-1  \\\n",
       "0        ...               0       1       0            0            1   \n",
       "1        ...               0       1       0            0            1   \n",
       "2        ...               0       1       0            0            1   \n",
       "3        ...               0       1       0            0            1   \n",
       "4        ...               0       1       0            0            1   \n",
       "\n",
       "   root_shell-0  root_shell-1  is_host_login-0  is_guest_login-0  \\\n",
       "0             1             0                1                 1   \n",
       "1             1             0                1                 1   \n",
       "2             1             0                1                 1   \n",
       "3             1             0                1                 1   \n",
       "4             1             0                1                 1   \n",
       "\n",
       "   is_guest_login-1  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "\n",
       "[5 rows x 123 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_features = ['is_guest_login-1','is_guest_login-0','is_host_login-0','root_shell-1','root_shell-0','logged_in-1','logged_in-0','land-1','land-0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['is_guest_login-1',\n",
       " 'is_guest_login-0',\n",
       " 'is_host_login-0',\n",
       " 'root_shell-1',\n",
       " 'root_shell-0',\n",
       " 'logged_in-1',\n",
       " 'logged_in-0',\n",
       " 'land-1',\n",
       " 'land-0']"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df_new[selected_features],df_new['enc_outcome'], test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF_Classifier = RandomForestClassifier(criterion='entropy', n_jobs=-1, random_state=0)\n",
    "RF_Classifier.fit(x_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "models = []\n",
    "models.append(('RandomForest Classifier', RF_Classifier))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================== {} {} Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.8458622045207663\n",
      "\n",
      "Model Accuracy:\n",
      " 0.8458621826574348\n",
      "\n",
      "Confusion matrix:\n",
      " [[52169     0 13710    14     0]\n",
      " [    8     0  1590     0     0]\n",
      " [  724     0 40167     0     0]\n",
      " [   15     0     5    22     0]\n",
      " [  698     0    60     6     0]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.97      0.79      0.87     65893\n",
      "          1       0.00      0.00      0.00      1598\n",
      "          2       0.72      0.98      0.83     40891\n",
      "          3       0.52      0.52      0.52        42\n",
      "          4       0.00      0.00      0.00       764\n",
      "\n",
      "avg / total       0.86      0.85      0.84    109188\n",
      "\n",
      "\n",
      "Plotting confusion matrix\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "for i, v in models:\n",
    "    scores = cross_val_score(v, x_train, y_train, cv=10)\n",
    "    accuracy = metrics.accuracy_score(y_train, v.predict(x_train))\n",
    "    confusion_matrix = metrics.confusion_matrix(y_train, v.predict(x_train))\n",
    "    classification = metrics.classification_report(y_train, v.predict(x_train))\n",
    "    print()\n",
    "    print('============================== {} {} Model Evaluation ==============================')\n",
    "    print()\n",
    "    print (\"Cross Validation Mean Score:\" \"\\n\", scores.mean())\n",
    "    print()\n",
    "    print (\"Model Accuracy:\" \"\\n\", accuracy)\n",
    "    print()\n",
    "    print(\"Confusion matrix:\" \"\\n\", confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification report:\" \"\\n\", classification) \n",
    "    print()\n",
    "    print('Plotting confusion matrix')\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(confusion_matrix, outcome)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}