phishing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # use for data manipulation and analysis\n",
    "import numpy as np # use for multi-dimensional array and matrix\n",
    "\n",
    "import seaborn as sns # use for high-level interface for drawing attractive and informative statistical graphics \n",
    "import matplotlib.pyplot as plt # It provides an object-oriented API for embedding plots into applications\n",
    "%matplotlib inline \n",
    "# It sets the backend of matplotlib to the 'inline' backend:\n",
    "import time # calculate time \n",
    "\n",
    "from sklearn.linear_model import LogisticRegression # algo use to predict good or bad\n",
    "from sklearn.naive_bayes import MultinomialNB # nlp algo use to predict good or bad\n",
    "\n",
    "from sklearn.model_selection import train_test_split # spliting the data between feature and target\n",
    "from sklearn.metrics import classification_report # gives whole report about metrics (e.g, recall,precision,f1_score,c_m)\n",
    "from sklearn.metrics import confusion_matrix # gives info about actual and predict\n",
    "from nltk.tokenize import RegexpTokenizer # regexp tokenizers use to split words from text  \n",
    "from nltk.stem.snowball import SnowballStemmer # stemmes words\n",
    "from sklearn.feature_extraction.text import CountVectorizer # create sparse matrix of words using regexptokenizes  \n",
    "from sklearn.pipeline import make_pipeline # use for combining all prerocessors techniuqes and algos\n",
    "\n",
    "from PIL import Image # getting images in notebook\n",
    "# from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator# creates words colud\n",
    "\n",
    "from bs4 import BeautifulSoup # use for scraping the data from website\n",
    "from selenium import webdriver # use for automation chrome \n",
    "import networkx as nx # for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.\n",
    "\n",
    "import pickle# use to dump model \n",
    "\n",
    "import warnings # ignores pink warnings \n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting selenium\n",
      "  Using cached selenium-3.141.0-py2.py3-none-any.whl (904 kB)\n",
      "Requirement already satisfied: urllib3 in c:\\users\\mail2\\anaconda3\\lib\\site-packages (from selenium) (1.25.11)\n",
      "Installing collected packages: selenium\n",
      "Successfully installed selenium-3.141.0\n"
     ]
    }
   ],
   "source": [
    "!pip install selenium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "phish_data = pd.read_csv('D:\\My Class\\CIP\\phishing_site_urls.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>URL</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nobell.it/70ffb52d079109dca5664cce6f317373782/...</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>www.dghjdgf.com/paypal.co.uk/cycgi-bin/webscrc...</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>serviciosbys.com/paypal.cgi.bin.get-into.herf....</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mail.printakid.com/www.online.americanexpress....</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>thewhiskeydregs.com/wp-content/themes/widescre...</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 URL Label\n",
       "0  nobell.it/70ffb52d079109dca5664cce6f317373782/...   bad\n",
       "1  www.dghjdgf.com/paypal.co.uk/cycgi-bin/webscrc...   bad\n",
       "2  serviciosbys.com/paypal.cgi.bin.get-into.herf....   bad\n",
       "3  mail.printakid.com/www.online.americanexpress....   bad\n",
       "4  thewhiskeydregs.com/wp-content/themes/widescre...   bad"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phish_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>URL</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>549341</th>\n",
       "      <td>23.227.196.215/</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549342</th>\n",
       "      <td>apple-checker.org/</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549343</th>\n",
       "      <td>apple-iclods.org/</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549344</th>\n",
       "      <td>apple-uptoday.org/</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549345</th>\n",
       "      <td>apple-search.info</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       URL Label\n",
       "549341     23.227.196.215/   bad\n",
       "549342  apple-checker.org/   bad\n",
       "549343   apple-iclods.org/   bad\n",
       "549344  apple-uptoday.org/   bad\n",
       "549345   apple-search.info   bad"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "phish_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 549346 entries, 0 to 549345\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   URL     549346 non-null  object\n",
      " 1   Label   549346 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 8.4+ MB\n"
     ]
    }
   ],
   "source": [
    "phish_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "URL      0\n",
       "Label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "phish_data.isnull().sum() # there is no missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a dataframe of classes counts\n",
    "label_counts = pd.DataFrame(phish_data.Label.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Label'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing target_col\n",
    "sns.set_style('darkgrid')\n",
    "sns.barplot(label_counts.index,label_counts.Label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = RegexpTokenizer(r'[A-Za-z]+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nobell.it/70ffb52d079109dca5664cce6f317373782/login.SkyPe.com/en/cgi-bin/verification/login/70ffb52d079109dca5664cce6f317373/index.php?cmd=_profile-ach&outdated_page_tmpl=p/gen/failed-to-load&nav=0.5.1&login_access=1322408526'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phish_data.URL[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['nobell',\n",
       " 'it',\n",
       " 'ffb',\n",
       " 'd',\n",
       " 'dca',\n",
       " 'cce',\n",
       " 'f',\n",
       " 'login',\n",
       " 'SkyPe',\n",
       " 'com',\n",
       " 'en',\n",
       " 'cgi',\n",
       " 'bin',\n",
       " 'verification',\n",
       " 'login',\n",
       " 'ffb',\n",
       " 'd',\n",
       " 'dca',\n",
       " 'cce',\n",
       " 'f',\n",
       " 'index',\n",
       " 'php',\n",
       " 'cmd',\n",
       " 'profile',\n",
       " 'ach',\n",
       " 'outdated',\n",
       " 'page',\n",
       " 'tmpl',\n",
       " 'p',\n",
       " 'gen',\n",
       " 'failed',\n",
       " 'to',\n",
       " 'load',\n",
       " 'nav',\n",
       " 'login',\n",
       " 'access']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this will be pull letter which matches to expression\n",
    "tokenizer.tokenize(phish_data.URL[0]) # using first row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting words tokenized ...\n",
      "Time taken 2.9229601000000116 sec\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('Getting words tokenized ...')\n",
    "t0= time.perf_counter()\n",
    "phish_data['text_tokenized'] = phish_data.URL.map(lambda t: tokenizer.tokenize(t)) # doing with all rows\n",
    "t1 = time.perf_counter() - t0\n",
    "print('Time taken',t1 ,'sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35088</th>\n",
       "      <td>amigocard.freeoda.com/promocacartaoamigo2012/c...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[amigocard, freeoda, com, promocacartaoamigo, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6275</th>\n",
       "      <td>'www.chinadcl.com/js/index.htm?us.battle.net/l...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[www, chinadcl, com, js, index, htm, us, battl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41087</th>\n",
       "      <td>'9d345009-a-62cb3a1a-s-sites.googlegroups.com/...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[d, a, cb, a, a, s, sites, googlegroups, com, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162803</th>\n",
       "      <td>current.com/shows/countdown/videos/keiths-spec...</td>\n",
       "      <td>good</td>\n",
       "      <td>[current, com, shows, countdown, videos, keith...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65607</th>\n",
       "      <td>tools.ietf.org/html/rfc1415</td>\n",
       "      <td>good</td>\n",
       "      <td>[tools, ietf, org, html, rfc]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      URL Label  \\\n",
       "35088   amigocard.freeoda.com/promocacartaoamigo2012/c...   bad   \n",
       "6275    'www.chinadcl.com/js/index.htm?us.battle.net/l...   bad   \n",
       "41087   '9d345009-a-62cb3a1a-s-sites.googlegroups.com/...   bad   \n",
       "162803  current.com/shows/countdown/videos/keiths-spec...  good   \n",
       "65607                         tools.ietf.org/html/rfc1415  good   \n",
       "\n",
       "                                           text_tokenized  \n",
       "35088   [amigocard, freeoda, com, promocacartaoamigo, ...  \n",
       "6275    [www, chinadcl, com, js, index, htm, us, battl...  \n",
       "41087   [d, a, cb, a, a, s, sites, googlegroups, com, ...  \n",
       "162803  [current, com, shows, countdown, videos, keith...  \n",
       "65607                       [tools, ietf, org, html, rfc]  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phish_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer(\"english\") # choose a language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting words stemmed ...\n",
      "Time taken 63.51811930000008 sec\n"
     ]
    }
   ],
   "source": [
    "print('Getting words stemmed ...')\n",
    "t0= time.perf_counter()\n",
    "phish_data['text_stemmed'] = phish_data['text_tokenized'].map(lambda l: [stemmer.stem(word) for word in l])\n",
    "t1= time.perf_counter() - t0\n",
    "print('Time taken',t1 ,'sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "      <th>text_stemmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>321351</th>\n",
       "      <td>empoweringeducators.com/index.html</td>\n",
       "      <td>good</td>\n",
       "      <td>[empoweringeducators, com, index, html]</td>\n",
       "      <td>[empoweringeduc, com, index, html]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458159</th>\n",
       "      <td>vent1.net/</td>\n",
       "      <td>good</td>\n",
       "      <td>[vent, net]</td>\n",
       "      <td>[vent, net]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475316</th>\n",
       "      <td>youtube.com/watch?v=ZfuZq7M4lTQ</td>\n",
       "      <td>good</td>\n",
       "      <td>[youtube, com, watch, v, ZfuZq, M, lTQ]</td>\n",
       "      <td>[youtub, com, watch, v, zfuzq, m, ltq]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434564</th>\n",
       "      <td>southernmodified.com/drivers/bloftin.htm</td>\n",
       "      <td>good</td>\n",
       "      <td>[southernmodified, com, drivers, bloftin, htm]</td>\n",
       "      <td>[southernmodifi, com, driver, bloftin, htm]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498428</th>\n",
       "      <td>kenestyonline.com/h782hd</td>\n",
       "      <td>bad</td>\n",
       "      <td>[kenestyonline, com, h, hd]</td>\n",
       "      <td>[kenestyonlin, com, h, hd]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             URL Label  \\\n",
       "321351        empoweringeducators.com/index.html  good   \n",
       "458159                                vent1.net/  good   \n",
       "475316           youtube.com/watch?v=ZfuZq7M4lTQ  good   \n",
       "434564  southernmodified.com/drivers/bloftin.htm  good   \n",
       "498428                  kenestyonline.com/h782hd   bad   \n",
       "\n",
       "                                        text_tokenized  \\\n",
       "321351         [empoweringeducators, com, index, html]   \n",
       "458159                                     [vent, net]   \n",
       "475316         [youtube, com, watch, v, ZfuZq, M, lTQ]   \n",
       "434564  [southernmodified, com, drivers, bloftin, htm]   \n",
       "498428                     [kenestyonline, com, h, hd]   \n",
       "\n",
       "                                       text_stemmed  \n",
       "321351           [empoweringeduc, com, index, html]  \n",
       "458159                                  [vent, net]  \n",
       "475316       [youtub, com, watch, v, zfuzq, m, ltq]  \n",
       "434564  [southernmodifi, com, driver, bloftin, htm]  \n",
       "498428                   [kenestyonlin, com, h, hd]  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phish_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting joiningwords ...\n",
      "Time taken 0.24450539999998 sec\n"
     ]
    }
   ],
   "source": [
    "print('Getting joiningwords ...')\n",
    "t0= time.perf_counter()\n",
    "phish_data['text_sent'] = phish_data['text_stemmed'].map(lambda l: ' '.join(l))\n",
    "t1= time.perf_counter() - t0\n",
    "print('Time taken',t1 ,'sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "      <th>text_stemmed</th>\n",
       "      <th>text_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>321683</th>\n",
       "      <td>encyclopedia.com/topic/Buster_Keaton.aspx</td>\n",
       "      <td>good</td>\n",
       "      <td>[encyclopedia, com, topic, Buster, Keaton, aspx]</td>\n",
       "      <td>[encyclopedia, com, topic, buster, keaton, aspx]</td>\n",
       "      <td>encyclopedia com topic buster keaton aspx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31136</th>\n",
       "      <td>www.dizmobiliario.net/php_recursos/stgeorge/st...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[www, dizmobiliario, net, php, recursos, stgeo...</td>\n",
       "      <td>[www, dizmobiliario, net, php, recurso, stgeor...</td>\n",
       "      <td>www dizmobiliario net php recurso stgeorg stge...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305938</th>\n",
       "      <td>classmates.com/directory/college/Calvary%20Cha...</td>\n",
       "      <td>good</td>\n",
       "      <td>[classmates, com, directory, college, Calvary,...</td>\n",
       "      <td>[classmat, com, directori, colleg, calvari, ch...</td>\n",
       "      <td>classmat com directori colleg calvari chapel b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469874</th>\n",
       "      <td>yelp.com/c/berkeley/chocolate</td>\n",
       "      <td>good</td>\n",
       "      <td>[yelp, com, c, berkeley, chocolate]</td>\n",
       "      <td>[yelp, com, c, berkeley, chocol]</td>\n",
       "      <td>yelp com c berkeley chocol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11397</th>\n",
       "      <td>x.co/juE1</td>\n",
       "      <td>bad</td>\n",
       "      <td>[x, co, juE]</td>\n",
       "      <td>[x, co, jue]</td>\n",
       "      <td>x co jue</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      URL Label  \\\n",
       "321683          encyclopedia.com/topic/Buster_Keaton.aspx  good   \n",
       "31136   www.dizmobiliario.net/php_recursos/stgeorge/st...   bad   \n",
       "305938  classmates.com/directory/college/Calvary%20Cha...  good   \n",
       "469874                      yelp.com/c/berkeley/chocolate  good   \n",
       "11397                                           x.co/juE1   bad   \n",
       "\n",
       "                                           text_tokenized  \\\n",
       "321683   [encyclopedia, com, topic, Buster, Keaton, aspx]   \n",
       "31136   [www, dizmobiliario, net, php, recursos, stgeo...   \n",
       "305938  [classmates, com, directory, college, Calvary,...   \n",
       "469874                [yelp, com, c, berkeley, chocolate]   \n",
       "11397                                        [x, co, juE]   \n",
       "\n",
       "                                             text_stemmed  \\\n",
       "321683   [encyclopedia, com, topic, buster, keaton, aspx]   \n",
       "31136   [www, dizmobiliario, net, php, recurso, stgeor...   \n",
       "305938  [classmat, com, directori, colleg, calvari, ch...   \n",
       "469874                   [yelp, com, c, berkeley, chocol]   \n",
       "11397                                        [x, co, jue]   \n",
       "\n",
       "                                                text_sent  \n",
       "321683          encyclopedia com topic buster keaton aspx  \n",
       "31136   www dizmobiliario net php recurso stgeorg stge...  \n",
       "305938  classmat com directori colleg calvari chapel b...  \n",
       "469874                         yelp com c berkeley chocol  \n",
       "11397                                            x co jue  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phish_data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sliceing classes\n",
    "bad_sites = phish_data[phish_data.Label == 'bad']\n",
    "good_sites = phish_data[phish_data.Label == 'good']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "      <th>text_stemmed</th>\n",
       "      <th>text_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nobell.it/70ffb52d079109dca5664cce6f317373782/...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[nobell, it, ffb, d, dca, cce, f, login, SkyPe...</td>\n",
       "      <td>[nobel, it, ffb, d, dca, cce, f, login, skype,...</td>\n",
       "      <td>nobel it ffb d dca cce f login skype com en cg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>www.dghjdgf.com/paypal.co.uk/cycgi-bin/webscrc...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[www, dghjdgf, com, paypal, co, uk, cycgi, bin...</td>\n",
       "      <td>[www, dghjdgf, com, paypal, co, uk, cycgi, bin...</td>\n",
       "      <td>www dghjdgf com paypal co uk cycgi bin webscrc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>serviciosbys.com/paypal.cgi.bin.get-into.herf....</td>\n",
       "      <td>bad</td>\n",
       "      <td>[serviciosbys, com, paypal, cgi, bin, get, int...</td>\n",
       "      <td>[serviciosbi, com, paypal, cgi, bin, get, into...</td>\n",
       "      <td>serviciosbi com paypal cgi bin get into herf s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mail.printakid.com/www.online.americanexpress....</td>\n",
       "      <td>bad</td>\n",
       "      <td>[mail, printakid, com, www, online, americanex...</td>\n",
       "      <td>[mail, printakid, com, www, onlin, americanexp...</td>\n",
       "      <td>mail printakid com www onlin americanexpress c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>thewhiskeydregs.com/wp-content/themes/widescre...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[thewhiskeydregs, com, wp, content, themes, wi...</td>\n",
       "      <td>[thewhiskeydreg, com, wp, content, theme, wide...</td>\n",
       "      <td>thewhiskeydreg com wp content theme widescreen...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 URL Label  \\\n",
       "0  nobell.it/70ffb52d079109dca5664cce6f317373782/...   bad   \n",
       "1  www.dghjdgf.com/paypal.co.uk/cycgi-bin/webscrc...   bad   \n",
       "2  serviciosbys.com/paypal.cgi.bin.get-into.herf....   bad   \n",
       "3  mail.printakid.com/www.online.americanexpress....   bad   \n",
       "4  thewhiskeydregs.com/wp-content/themes/widescre...   bad   \n",
       "\n",
       "                                      text_tokenized  \\\n",
       "0  [nobell, it, ffb, d, dca, cce, f, login, SkyPe...   \n",
       "1  [www, dghjdgf, com, paypal, co, uk, cycgi, bin...   \n",
       "2  [serviciosbys, com, paypal, cgi, bin, get, int...   \n",
       "3  [mail, printakid, com, www, online, americanex...   \n",
       "4  [thewhiskeydregs, com, wp, content, themes, wi...   \n",
       "\n",
       "                                        text_stemmed  \\\n",
       "0  [nobel, it, ffb, d, dca, cce, f, login, skype,...   \n",
       "1  [www, dghjdgf, com, paypal, co, uk, cycgi, bin...   \n",
       "2  [serviciosbi, com, paypal, cgi, bin, get, into...   \n",
       "3  [mail, printakid, com, www, onlin, americanexp...   \n",
       "4  [thewhiskeydreg, com, wp, content, theme, wide...   \n",
       "\n",
       "                                           text_sent  \n",
       "0  nobel it ffb d dca cce f login skype com en cg...  \n",
       "1  www dghjdgf com paypal co uk cycgi bin webscrc...  \n",
       "2  serviciosbi com paypal cgi bin get into herf s...  \n",
       "3  mail printakid com www onlin americanexpress c...  \n",
       "4  thewhiskeydreg com wp content theme widescreen...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_sites.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "      <th>text_stemmed</th>\n",
       "      <th>text_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18231</th>\n",
       "      <td>esxcc.com/js/index.htm?us.battle.net/noghn/en/...</td>\n",
       "      <td>good</td>\n",
       "      <td>[esxcc, com, js, index, htm, us, battle, net, ...</td>\n",
       "      <td>[esxcc, com, js, index, htm, us, battl, net, n...</td>\n",
       "      <td>esxcc com js index htm us battl net noghn en r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18232</th>\n",
       "      <td>www\u000eeira¯&amp;nvinip¿ncH¯wVö%ÆåyDaHðû/ÏyEùu\u0003Ë\\nÓ\u00176...</td>\n",
       "      <td>good</td>\n",
       "      <td>[www, eira, nvinip, ncH, wV, yDaH, yE, u, rT, ...</td>\n",
       "      <td>[www, eira, nvinip, nch, wv, ydah, ye, u, rt, ...</td>\n",
       "      <td>www eira nvinip nch wv ydah ye u rt u g m i xz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18233</th>\n",
       "      <td>'www.institutocgr.coo/web/media/syqvem/dk-\u000fóij...</td>\n",
       "      <td>good</td>\n",
       "      <td>[www, institutocgr, coo, web, media, syqvem, d...</td>\n",
       "      <td>[www, institutocgr, coo, web, media, syqvem, d...</td>\n",
       "      <td>www institutocgr coo web media syqvem dk ij r ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18234</th>\n",
       "      <td>\u0011\u0018Yìê\f",
       "koãÕ»Î§DéÎ\u0002l½ñ¡ââqtò¸/à; Í</td>\n",
       "      <td>good</td>\n",
       "      <td>[Y, ko, D, l, qt]</td>\n",
       "      <td>[y, ko, d, l, qt]</td>\n",
       "      <td>y ko d l qt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>ruta89fm.com/images/AS@Vies/1i75cf7b16vc&lt;F\u0015d16...</td>\n",
       "      <td>good</td>\n",
       "      <td>[ruta, fm, com, images, AS, Vies, i, cf, b, vc...</td>\n",
       "      <td>[ruta, fm, com, imag, as, vie, i, cf, b, vc, f...</td>\n",
       "      <td>ruta fm com imag as vie i cf b vc f d b g sd v...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     URL Label  \\\n",
       "18231  esxcc.com/js/index.htm?us.battle.net/noghn/en/...  good   \n",
       "18232  www\u000eeira¯&nvinip¿ncH¯wVö%ÆåyDaHðû/ÏyEùu\u0003Ë\\nÓ\u00176...  good   \n",
       "18233  'www.institutocgr.coo/web/media/syqvem/dk-\u000fóij...  good   \n",
       "18234                  \u0011\u0018Yìê\n",
       "koãÕ»Î§DéÎ\u0002l½ñ¡ââqtò¸/à; Í  good   \n",
       "18236  ruta89fm.com/images/AS@Vies/1i75cf7b16vc<F\u0015d16...  good   \n",
       "\n",
       "                                          text_tokenized  \\\n",
       "18231  [esxcc, com, js, index, htm, us, battle, net, ...   \n",
       "18232  [www, eira, nvinip, ncH, wV, yDaH, yE, u, rT, ...   \n",
       "18233  [www, institutocgr, coo, web, media, syqvem, d...   \n",
       "18234                                  [Y, ko, D, l, qt]   \n",
       "18236  [ruta, fm, com, images, AS, Vies, i, cf, b, vc...   \n",
       "\n",
       "                                            text_stemmed  \\\n",
       "18231  [esxcc, com, js, index, htm, us, battl, net, n...   \n",
       "18232  [www, eira, nvinip, nch, wv, ydah, ye, u, rt, ...   \n",
       "18233  [www, institutocgr, coo, web, media, syqvem, d...   \n",
       "18234                                  [y, ko, d, l, qt]   \n",
       "18236  [ruta, fm, com, imag, as, vie, i, cf, b, vc, f...   \n",
       "\n",
       "                                               text_sent  \n",
       "18231  esxcc com js index htm us battl net noghn en r...  \n",
       "18232  www eira nvinip nch wv ydah ye u rt u g m i xz...  \n",
       "18233  www institutocgr coo web media syqvem dk ij r ...  \n",
       "18234                                        y ko d l qt  \n",
       "18236  ruta fm com imag as vie i cf b vc f d b g sd v...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_sites.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[WDM] - ====== WebDriver manager ======\n",
      "[WDM] - Current google-chrome version is 89.0.4389\n",
      "[WDM] - Get LATEST driver version for 89.0.4389\n",
      "[WDM] - Driver [C:\\Users\\mail2\\.wdm\\drivers\\chromedriver\\win32\\89.0.4389.23\\chromedriver.exe] found in cache\n"
     ]
    }
   ],
   "source": [
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "\n",
    "browser= webdriver.Chrome(ChromeDriverManager().install())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting webdriver-manager\n",
      "  Downloading webdriver_manager-3.3.0-py2.py3-none-any.whl (16 kB)\n",
      "Requirement already satisfied: requests in c:\\users\\mail2\\anaconda3\\lib\\site-packages (from webdriver-manager) (2.24.0)\n",
      "Collecting configparser\n",
      "  Downloading configparser-5.0.2-py3-none-any.whl (19 kB)\n",
      "Collecting crayons\n",
      "  Downloading crayons-0.4.0-py2.py3-none-any.whl (4.6 kB)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\mail2\\anaconda3\\lib\\site-packages (from requests->webdriver-manager) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\mail2\\anaconda3\\lib\\site-packages (from requests->webdriver-manager) (2020.6.20)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\mail2\\anaconda3\\lib\\site-packages (from requests->webdriver-manager) (2.10)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\mail2\\anaconda3\\lib\\site-packages (from requests->webdriver-manager) (1.25.11)\n",
      "Requirement already satisfied: colorama in c:\\users\\mail2\\anaconda3\\lib\\site-packages (from crayons->webdriver-manager) (0.4.4)\n",
      "Installing collected packages: configparser, crayons, webdriver-manager\n",
      "Successfully installed configparser-5.0.2 crayons-0.4.0 webdriver-manager-3.3.0\n"
     ]
    }
   ],
   "source": [
    "pip install webdriver-manager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_urls = ['https://www.ezeephones.com/','https://www.ezeephones.com/about-us'] #here i take phishing sites \n",
    "links_with_text = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "ename": "WebDriverException",
     "evalue": "Message: unknown error: net::ERR_CONNECTION_TIMED_OUT\n  (Session info: chrome=89.0.4389.128)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mWebDriverException\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-48-ce3768a8e09e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0murl\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlist_urls\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mbrowser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0msoup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbrowser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpage_source\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"html.parser\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msoup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mhref\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'href'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\selenium\\webdriver\\remote\\webdriver.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, url)\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[0mLoads\u001b[0m \u001b[0ma\u001b[0m \u001b[0mweb\u001b[0m \u001b[0mpage\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mbrowser\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    332\u001b[0m         \"\"\"\n\u001b[1;32m--> 333\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCommand\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGET\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'url'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    334\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    335\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\selenium\\webdriver\\remote\\webdriver.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, driver_command, params)\u001b[0m\n\u001b[0;32m    319\u001b[0m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand_executor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdriver_command\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    320\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 321\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    322\u001b[0m             response['value'] = self._unwrap_value(\n\u001b[0;32m    323\u001b[0m                 response.get('value', None))\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\selenium\\webdriver\\remote\\errorhandler.py\u001b[0m in \u001b[0;36mcheck_response\u001b[1;34m(self, response)\u001b[0m\n\u001b[0;32m    240\u001b[0m                 \u001b[0malert_text\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'alert'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'text'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    241\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mexception_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscreen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacktrace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malert_text\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 242\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mexception_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscreen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacktrace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    243\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    244\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_value_or_default\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mWebDriverException\u001b[0m: Message: unknown error: net::ERR_CONNECTION_TIMED_OUT\n  (Session info: chrome=89.0.4389.128)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for url in list_urls:\n",
    "    browser.get(url)\n",
    "    soup = BeautifulSoup(browser.page_source,\"html.parser\")\n",
    "    for line in soup.find_all('a'):\n",
    "        href = line.get('href')\n",
    "        links_with_text.append([url, href])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.DataFrame(links_with_text, columns=[\"from\", \"to\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GA = nx.from_pandas_edgelist(df, source=\"from\", target=\"to\")\n",
    "nx.draw(GA, with_labels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create cv object\n",
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "feature = cv.fit_transform(phish_data.text_sent) #transform all text which we tokenize and stemed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature[:5].toarray() # convert sparse matrix into array to print transformed features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(feature, phish_data.Label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# create lr object\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9643213409350722"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scores_ml = {}\n",
    "Scores_ml['Logistic Regression'] = np.round(lr.score(testX,testY),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.978024751886488\n",
      "Testing Accuracy : 0.9643213409350722\n",
      "\n",
      "CLASSIFICATION REPORT\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Bad       0.90      0.97      0.94     36501\n",
      "        Good       0.99      0.96      0.98    100836\n",
      "\n",
      "    accuracy                           0.96    137337\n",
      "   macro avg       0.95      0.97      0.96    137337\n",
      "weighted avg       0.97      0.96      0.96    137337\n",
      "\n",
      "\n",
      "CONFUSION MATRIX\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Training Accuracy :',lr.score(trainX,trainY))\n",
    "print('Testing Accuracy :',lr.score(testX,testY))\n",
    "con_mat = pd.DataFrame(confusion_matrix(lr.predict(testX), testY),\n",
    "            columns = ['Predicted:Bad', 'Predicted:Good'],\n",
    "            index = ['Actual:Bad', 'Actual:Good'])\n",
    "\n",
    "\n",
    "print('\\nCLASSIFICATION REPORT\\n')\n",
    "print(classification_report(lr.predict(testX), testY,\n",
    "                            target_names =['Bad','Good']))\n",
    "\n",
    "print('\\nCONFUSION MATRIX')\n",
    "plt.figure(figsize= (6,4))\n",
    "sns.heatmap(con_mat, annot = True,fmt='d',cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create mnb object\n",
    "mnb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "mnb.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9582414061760487"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnb.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Scores_ml['MultinomialNB'] = np.round(mnb.score(testX,testY),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.9739228997424814\n",
      "Testing Accuracy : 0.9582414061760487\n",
      "\n",
      "CLASSIFICATION REPORT\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Bad       0.92      0.94      0.93     38218\n",
      "        Good       0.98      0.97      0.97     99119\n",
      "\n",
      "    accuracy                           0.96    137337\n",
      "   macro avg       0.95      0.95      0.95    137337\n",
      "weighted avg       0.96      0.96      0.96    137337\n",
      "\n",
      "\n",
      "CONFUSION MATRIX\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Training Accuracy :',mnb.score(trainX,trainY))\n",
    "print('Testing Accuracy :',mnb.score(testX,testY))\n",
    "con_mat = pd.DataFrame(confusion_matrix(mnb.predict(testX), testY),\n",
    "            columns = ['Predicted:Bad', 'Predicted:Good'],\n",
    "            index = ['Actual:Bad', 'Actual:Good'])\n",
    "\n",
    "\n",
    "print('\\nCLASSIFICATION REPORT\\n')\n",
    "print(classification_report(mnb.predict(testX), testY,\n",
    "                            target_names =['Bad','Good']))\n",
    "\n",
    "print('\\nCONFUSION MATRIX')\n",
    "plt.figure(figsize= (6,4))\n",
    "sns.heatmap(con_mat, annot = True,fmt='d',cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Accuracy'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD5CAYAAADP2jUWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZN0lEQVR4nO3deXRU9d3H8c/MZGcCYQlLD5uERYpSNhf0BOsSK0E5EiyTqIN1w41aISDogZiwJCmL2IJSlWIjIIkiKlDtOQbQtOmpAjVgpIACBrAcE5FAJyNMkrnPH55OzQPJQMMNlN/79Vfu3Mydb8LNe27unQkOy7IsAQCM4jzfAwAAWh7xBwADEX8AMBDxBwADEX8AMBDxBwAD2Rb/7du3y+v1nnL7pk2bNHbsWHk8Hr3++ut2PTwAoAkRdmz05Zdf1rp16xQbG9vg9traWuXl5WnNmjWKjY1VRkaGrr/+eiUmJja5vWAwqPp63o4AAGcjMtLV6Dpb4t+9e3ctXrxYTz75ZIPb9+7dq+7du6tNmzaSpKFDh2rr1q0aOXJkk9urr7dUXe23Y1QAuGglJsY3us6W+P/sZz/ToUOHTrnd5/MpPv4/w7Rq1Uo+ny/s9lwuhxIS4s7pjABgMlvi3xi3262amprQck1NTYMng8Zw5A8AZ6+pI/8WfbVPUlKSKioqVF1drUAgoK1bt2rw4MEtOQIAQC105L9+/Xr5/X55PB5Nnz5d999/vyzL0tixY9WpU6eWGAEA8AOO/4W/6llbW89pHwA4SxfMaR8AwIWB+AOAgYg/ABiI+AOAgVr0df7nk7t1jGKjI8/3GLjAfHeyVr7jJ87rDO3aRMoVFXNeZ8CFpz5wQt8eq7Vt+8bEPzY6UkOnvnq+x8AFZtv88fLp/MbfFRWjA7MuP68z4MLTPetTSfbFn9M+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAgW+IfDAaVlZUlj8cjr9erioqKBuvXrVunMWPGaOzYsXrttdfsGAEA0IQIOzZaXFysQCCgoqIilZWVKT8/X0uXLg2tnzdvnjZs2KC4uDiNGjVKo0aNUps2bewYBQBwGrbEf9u2bUpOTpYkDRo0SOXl5Q3W9+vXT//6178UEREhy7LkcDjsGAMA0Ahb4u/z+eR2u0PLLpdLdXV1ioj4/uH69OmjsWPHKjY2VikpKWrdunWT23O5HEpIiLNjVIB9CxcsO/dNW+LvdrtVU1MTWg4Gg6Hw79q1Sx988IE2btyouLg4TZ06Ve+9955GjhzZ6Pbq6y1VV/ubNVNiYnyz7o+LV3P3reZi30Rj7OyeLRd8hwwZopKSEklSWVmZ+vbtG1oXHx+vmJgYRUdHy+VyqV27djp+/LgdYwAAGmHLkX9KSopKS0uVnp4uy7KUm5ur9evXy+/3y+PxyOPx6M4771RkZKS6d++uMWPG2DEGAKARtsTf6XRq1qxZDW5LSkoKfZyRkaGMjAw7HhoAcAZ4kxcAGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGIj4A4CBiD8AGCjCjo0Gg0FlZ2dr9+7dioqK0pw5c9SjR4/Q+h07dig/P1+WZSkxMVHz589XdHS0HaMAAE7DliP/4uJiBQIBFRUVKTMzU/n5+aF1lmVp5syZysvL0+rVq5WcnKyvvvrKjjEAAI2w5ch/27ZtSk5OliQNGjRI5eXloXX79+9XQkKCCgoKtGfPHl133XXq1auXHWMAABphS/x9Pp/cbndo2eVyqa6uThERETp69Kg++eQTzZw5Uz169NDDDz+syy67TMOHD290ey6XQwkJcXaMCrBv4YJl574ZNv61tbWKjIw8q4263W7V1NSEloPBoCIivn+ohIQE9ejRQ71795YkJScnq7y8vMn419dbqq72n9UM/19iYnyz7o+LV3P3reZi30Rj7Oxe2HP+aWlpmjt3rvbs2XPGDzhkyBCVlJRIksrKytS3b9/Qum7duqmmpkYVFRWSpK1bt6pPnz5nvG0AQPOFPfJ/55139Oc//1lLlizR0aNHNXr0aKWmpqpVq1aN3iclJUWlpaVKT0+XZVnKzc3V+vXr5ff75fF4NHfuXGVmZsqyLA0ePFg//elPz+XXBAAIw2FZlhXukyzLUklJidasWaOKigrFxcVpzJgx8ng8LTGjamvrz8mvP0OnvnqOJsLFYtv88aqq+td5nSExMV4HZl1+XmfAhad71qfN3jebOu0T9sh/3rx52rhxo6688ko9+OCDGjhwoILBoNLS0los/gCAcyts/Hv27Km33npLcXFxqq2tlSQ5nU4tWbLE9uEAAPYIe8HXsiw999xzkqSHHnpIb7/9tiSpa9euds4FALBR2PgXFhYqMzNTkvTiiy9q9erVtg8FALBX2Pg7nc7Q392JjIyUw+GwfSgAgL3CnvO/8cYbdeedd2rgwIH67LPPdMMNN7TEXAAAG4WN/6OPPqrrr79e+/fv1+23365LL720JeYCANgo7GmfiooKlZSUaN++fSouLlZWVlZLzAUAsFHY+E+bNk2S9Pe//12HDh1SdXW13TMBAGwWNv4xMTF66KGH1KlTJ+Xn5+ubb75pibkAADY6o9f5V1VVye/3y+/369ixYy0xFwDARmHjP3HiRBUXF2v06NG68cYbNWLEiJaYCwBgo7Cv9tmxY4fuv/9+Sd+/7BMA8L8v7JH/hx9+qPr6+paYBQDQQsIe+R89elTJycnq2rWrHA6HHA6HCgsLW2I2AIBNwsb/d7/7XUvMAQBoQWHj/9Zbb51y28SJE20ZBgDQMsLGv0OHDpK+f8nnzp07FQwGbR8KAGCvsPFPT09vsPzAAw/YNgwAoGWEjf/+/ftDH1dVVenw4cO2DgQAsF/Y+GdlZcnhcMiyLMXExOjJJ59sibkAADYKG/9ly5Zp7969+vGPf6zi4mJdc801LTEXAMBGYd/kNXXqVG3fvl3S96eApk+fbvtQAAB7hY3/119/rYyMDEnSgw8+qMrKStuHAgDYK2z8pf9c9D1w4AAv9QSAi0DYc/5PP/20nnjiCR05ckQdO3ZUTk5OS8wFALBR2Pj3799feXl5oQu+/B++APC/L+xpnylTpnDBFwAuMlzwBQADndUF34qKCi74AsBF4Kwu+MbExGjMmDEtMRcAwEZhj/x/8pOfaPbs2brmmmv03Xff6ciRIy0xFwDARo0e+QcCAf3xj3/UqlWrFBUVJZ/Pp40bNyomJqYl5wMA2KDRI/8bbrhBu3fv1oIFC/Taa6+pY8eOhB8ALhKNHvmPHz9eGzZs0FdffaU77rhDlmW15FwAABs1euQ/YcIErVu3Tl6vVxs2bFB5ebnmz5+vPXv2tOR8AAAbhL3ge+WVV2r+/Pl6//331blz5zP6e/7BYFBZWVnyeDzyer2qqKg47efNnDlTCxYsOPupAQDNckav85ek1q1by+v16u233w77ucXFxQoEAioqKlJmZqby8/NP+ZzCwkJ+iwCA8+SM4382tm3bpuTkZEnSoEGDVF5e3mD9J598ou3bt8vj8djx8ACAMMK+yeu/4fP55Ha7Q8sul0t1dXWKiIhQZWWllixZoiVLlui99947o+25XA4lJMTZMSrAvoULlp37pi3xd7vdqqmpCS0Hg0FFRHz/UH/605909OhRTZgwQVVVVTpx4oR69eqltLS0RrdXX2+putrfrJkSE+ObdX9cvJq7bzUX+yYaY2f3bIn/kCFDtHnzZqWmpqqsrEx9+/YNrRs/frzGjx8vSVq7dq327dvXZPgBAOeeLfFPSUlRaWmp0tPTZVmWcnNztX79evn9fs7zA8AFwJb4O51OzZo1q8FtSUlJp3weR/wAcH7Y8mofAMCFjfgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYiPgDgIGIPwAYKMKOjQaDQWVnZ2v37t2KiorSnDlz1KNHj9D6DRs2qKCgQC6XS3379lV2dracTp6HAKCl2FLc4uJiBQIBFRUVKTMzU/n5+aF1J06c0HPPPadXX31VhYWF8vl82rx5sx1jAAAaYUv8t23bpuTkZEnSoEGDVF5eHloXFRWlwsJCxcbGSpLq6uoUHR1txxgAgEbYctrH5/PJ7XaHll0ul+rq6hQRESGn06kOHTpIklasWCG/369rr722ye25XA4lJMTZMSrAvoULlp37pi3xd7vdqqmpCS0Hg0FFREQ0WJ4/f77279+vxYsXy+FwNLm9+npL1dX+Zs2UmBjfrPvj4tXcfau52DfRGDu7Z8tpnyFDhqikpESSVFZWpr59+zZYn5WVpZMnT+qFF14Inf4BALQcW478U1JSVFpaqvT0dFmWpdzcXK1fv15+v1+XXXaZ1qxZo2HDhumee+6RJI0fP14pKSl2jAIAOA1b4u90OjVr1qwGtyUlJYU+3rVrlx0PCwA4Q7y4HgAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMRPwBwEDEHwAMZEv8g8GgsrKy5PF45PV6VVFR0WD9pk2bNHbsWHk8Hr3++ut2jAAAaIIt8S8uLlYgEFBRUZEyMzOVn58fWldbW6u8vDwtX75cK1asUFFRkaqqquwYAwDQCFviv23bNiUnJ0uSBg0apPLy8tC6vXv3qnv37mrTpo2ioqI0dOhQbd261Y4xAACNiLBjoz6fT263O7TscrlUV1eniIgI+Xw+xcfHh9a1atVKPp+vye1FRrqUmBjf5OeciW3zxzd7G7j4nIt9q7m6Z316vkfABcjOfdOWI3+3262amprQcjAYVERExGnX1dTUNHgyAADYz5b4DxkyRCUlJZKksrIy9e3bN7QuKSlJFRUVqq6uViAQ0NatWzV48GA7xgAANMJhWZZ1rjcaDAaVnZ2tPXv2yLIs5ebmaufOnfL7/fJ4PNq0aZOef/55WZalsWPH6q677jrXIwAAmmBL/AEAFzbe5AUABiL+AGAg4n8OfPTRR5o0aVKztvHSSy9px44dja5fuXKlJKmkpERFRUVnNNPw4cPl9Xrl9XqVlpamxx9/XIFAoFlzNtfEiRPP6+MjvI8++kj9+vXTu+++2+D22267TdOnTz/tfdauXasFCxZIkoqKilRbW6t//OMfWrJkyTmfL9zPwOLFi7V69WodOnRIAwYMaPA+o9WrV2vx4sWSpBtuuEF33XWX7r77bqWlpWnVqlXnfNYLmS2v88fZmzBhQpPrly5dqrvvvlsjRow4421effXVWrRoUWg5MzNTmzZt0i233PJfz9lcdsQA516vXr20YcMGpaamSpJ2796t77777ozu++KLL+r2229X//791b9//3M+29n8DLjdbj311FN68803FRUVdcr65cuXKzo6WoFAQKmpqbrlllvUvn37cznuBYv426i0tFTPPfecoqOjlZCQoNzcXMXHxysnJ0fl5eXq0KGDvvrqKy1dulRLlixRamqqunXrpqeeekoRERFyuVyaN2+e1q5dq2PHjik7O1sDBw7Uvn37NGXKFL3wwgsqLi5WfX29MjIylJ6e3ugsgUBAlZWVatOmjSRp4cKF2rJliyzL0i9+8QuNHDlSO3bsUE5Ojlq1aqX27dsrOjpaEydO1COPPKKEhASNGDFCI0aM0Jw5cyQp9DXV1tbqiSeekGVZqq2tVU5Ojnr27Klf/epX8vl8OnHihKZOnaqrrrpK1157rUpLS7Vz507Nnj1bLpdL0dHRmj17toLBoDIzM9W5c2cdPHhQl19+uXJyclrk3woNXXrppfryyy91/PhxtW7dWuvWrdNtt92mw4cPh/4NJWnSpEkN9rs33nhDVVVVmjRpku655x4VFhZq0aJFuvnmmzVkyBDt379f7du31+LFixUMBvX000/r4MGDqq+v17333qvU1FR5vV7169dPn3/+ueLi4jRs2DD95S9/0fHjx7V8+XJt3Lgx9DOwcOFClZeXq6amRklJScrLy2vwdfTo0UPDhg3TokWLNG3atEa/3hMnTig6Otqo9xwRf5tYlqWZM2dq9erV6tSpkwoKCrR06VINHTpU1dXVWrNmjb799lvdfPPNDe7317/+VQMGDND06dO1detWHTt2TI888ohWrlyp7OxsrV27VpK0c+dOlZSU6I033lAgENDChQtlWZYcDkdoW3/729/k9Xp15MgROZ1OjRs3TsOHD9eHH36oQ4cOqbCwUCdPntS4ceN07bXX6plnntG8efPUp08fLVq0SF9//bUkqaqqKnTkNG7cOOXm5qp379564403tGzZMg0ePFjx8fFauHChvvjiC/l8Ph04cEDffPON/vCHP+jIkSP68ssvG3ydM2bM0Ny5c9W/f38VFxcrPz9fTz75pL788kv9/ve/V2xsrG666SZVVVUpMTHR3n8snFZKSoref/99paWlaceOHXrwwQd1+PDhJu/z85//XEuXLtWiRYtUVlYWuv3gwYMqKChQly5dlJ6erk8//VTl5eVq27at5s+fL5/Pp7S0NF199dWSpIEDB2rGjBm6//77FRMTo1deeUXTpk3Tli1bQtv0+Xxq3bq1XnnlFQWDQY0aNSq0z/7QE088oTvuuOO0f0bmvvvuk8Ph0L59+3TTTTcpMjLyv/xu/e/hnL9Njh49KrfbrU6dOkmSrrjiCn3++efat2+fBg0aJElq166devXq1eB+d9xxh9q2basHHnhAq1atksvlOu329+/fr4EDB8rlcik2NlYzZsxoEH7p+9M+K1as0KpVqxQZGamuXbtKkvbs2aPPPvtMXq9XDzzwgOrq6vTPf/5TlZWV6tOnjyRp6NChoe107do19Cvz3r17lZOTI6/XqzfffFOVlZUaMWKErrjiCj366KP67W9/K6fTqT59+uiuu+7S5MmTlZOTo2Aw2GC2ysrK0CmBf39vJKl79+5yu91yuVxKTEzUyZMnz/p7j3Pjtttu07vvvqstW7Zo2LBhp/2cM32leNu2bdWlSxdJUpcuXXTy5Ent3btXV1xxhaTvT88kJSXp4MGDkqQBAwZIklq3bq3evXuHPv7h/hAdHa1vv/1WkydPVlZWlvx+v2pra0957KioKOXl5WnGjBmnnLpavny5Vq5cqQ8++EAHDhzQunXrzujruRgQf5u0bdtWPp9PlZWVkqSPP/5YPXv2VJ8+fUJHRMeOHTvliHjjxo0aOnSoCgoKdMstt2jZsmWSTv0h69Wrl3bu3KlgMKja2lrde++9jV7M/ffR1YwZM1RZWalevXrpqquu0ooVK1RQUKCRI0eqa9eu6ty5s7744gtJ0vbt20P3dzr/s5tccskl+vWvf60VK1Zo6tSpuu666/TRRx+pY8eOWr58uR555BE9++yz2r17t2pqavTSSy8pPz9fs2fPbjBTx44dtWvXLknSli1b1LNnT0k65QkM50+3bt3k9/u1YsUKjR49OnR7XV2dampqFAgEQvvLDzkcjlOe7E/375qUlBQ6Gvf5fNqzZ0/oAOVMlJSU6PDhw3r22Wc1efJknThxotEnowEDBujWW2/Vyy+/fNr1UVFRat++/WmfPC5WnPY5R0pLS5WWlhZaXrhwoebMmaNf/vKXcjgcatOmjfLy8tS2bVuVlJQoPT1dHTp0UExMTINfNS+77DJNnTpVixcvltPp1FNPPSXp+x+UKVOm6JprrpEk9e/fX8nJycrIyFAwGFRGRsZpL2j9W+/eveX1ejVnzhz95je/0ccff6w777xTfr9fN910k9xut5555hk9/fTTiouLU2RkZOi3lh/Kzs7WtGnTVF9fL0maO3euEhISNGnSJBUUFMjpdOqxxx5Tz5499fzzz+vtt99WZGSkHn/88QbbmTNnjmbPni3LsuRyuZSbm/vff/Nhm9TUVL3zzju65JJLQkfl48ePl8fjUdeuXfWjH/3olPsMGzZMEyZM0GOPPdbktseNG6eZM2cqIyNDJ0+e1MSJE8/qYuvAgQP1wgsvaNy4cYqKilK3bt1CB1un8/DDD2vz5s0NbrvvvvvkdDoVDAbVuXPnBk9yFzve4dvC9u7dq127dmnUqFE6evSobr31Vm3evLnJcLeUVatWaeTIkWrXrp0WLVqkyMhIXpoJXKQ48m9hXbp00YIFC1RQUKD6+npNmTLlggi/JLVv31733Xef4uLiFB8f3+A/4QFwceHIHwAMxAVfADAQ8QcAAxF/ADAQ8QcAAxF/ADAQ8QcAA/0fGGel/ojpAE8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "acc = pd.DataFrame.from_dict(Scores_ml,orient = 'index',columns=['Accuracy'])\n",
    "sns.set_style('darkgrid')\n",
    "sns.barplot(acc.index,acc.Accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pipeline_ls = make_pipeline(CountVectorizer(tokenizer = RegexpTokenizer(r'[A-Za-z]+').tokenize,stop_words='english'), LogisticRegression())\n",
    "##(r'\\b(?:http|ftp)s?://\\S*\\w|\\w+|[^\\w\\s]+') ([a-zA-Z]+)([0-9]+) -- these tolenizers giving me low accuray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(phish_data.URL, phish_data.Label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('countvectorizer',\n",
       "                 CountVectorizer(stop_words='english',\n",
       "                                 tokenizer=<bound method RegexpTokenizer.tokenize of RegexpTokenizer(pattern='[A-Za-z]+', gaps=False, discard_empty=True, flags=re.UNICODE|re.MULTILINE|re.DOTALL)>)),\n",
       "                ('logisticregression', LogisticRegression())])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_ls.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9659378026314831"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pipeline_ls.score(testX,testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy : 0.9792577346611361\n",
      "Testing Accuracy : 0.9659378026314831\n",
      "\n",
      "CLASSIFICATION REPORT\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Bad       0.91      0.97      0.94     36866\n",
      "        Good       0.99      0.97      0.98    100471\n",
      "\n",
      "    accuracy                           0.97    137337\n",
      "   macro avg       0.95      0.97      0.96    137337\n",
      "weighted avg       0.97      0.97      0.97    137337\n",
      "\n",
      "\n",
      "CONFUSION MATRIX\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Training Accuracy :',pipeline_ls.score(trainX,trainY))\n",
    "print('Testing Accuracy :',pipeline_ls.score(testX,testY))\n",
    "con_mat = pd.DataFrame(confusion_matrix(pipeline_ls.predict(testX), testY),\n",
    "            columns = ['Predicted:Bad', 'Predicted:Good'],\n",
    "            index = ['Actual:Bad', 'Actual:Good'])\n",
    "\n",
    "\n",
    "print('\\nCLASSIFICATION REPORT\\n')\n",
    "print(classification_report(pipeline_ls.predict(testX), testY,\n",
    "                            target_names =['Bad','Good']))\n",
    "\n",
    "print('\\nCONFUSION MATRIX')\n",
    "plt.figure(figsize= (6,4))\n",
    "sns.heatmap(con_mat, annot = True,fmt='d',cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(pipeline_ls,open('phishing.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9659378026314831\n"
     ]
    }
   ],
   "source": [
    "loaded_model = pickle.load(open('phishing.pkl', 'rb'))\n",
    "result = loaded_model.score(testX,testY)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bad' 'bad' 'bad' 'bad']\n",
      "******************************\n",
      "['good' 'good' 'good' 'good']\n"
     ]
    }
   ],
   "source": [
    "predict_bad = ['yeniik.com.tr/wp-admin/js/login.alibaba.com/login.jsp.php','fazan-pacir.rs/temp/libraries/ipad','tubemoviez.exe','svision-online.de/mgfi/administrator/components/com_babackup/classes/fx29id1.txt']\n",
    "predict_good = ['youtube.com/','youtube.com/watch?v=qI0TQJI3vdU','retailhellunderground.com/','restorevisioncenters.com/html/technology.html']\n",
    "loaded_model = pickle.load(open('phishing.pkl', 'rb'))\n",
    "#predict_bad = vectorizers.transform(predict_bad)\n",
    "# predict_good = vectorizer.transform(predict_good)\n",
    "result = loaded_model.predict(predict_bad)\n",
    "result2 = loaded_model.predict(predict_good)\n",
    "print(result)\n",
    "print(\"*\"*30)\n",
    "print(result2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "This event loop is already running",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-73-57f7bb703704>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     22\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'__main__'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m         \u001b[0muvicorn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mhost\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"127.0.0.1\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mport\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m8000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\uvicorn\\main.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(app, **kwargs)\u001b[0m\n\u001b[0;32m    384\u001b[0m         \u001b[0msupervisor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 386\u001b[1;33m         \u001b[0mserver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    387\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\uvicorn\\server.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, sockets)\u001b[0m\n\u001b[0;32m     47\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetup_event_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[0mloop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masyncio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_event_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m         \u001b[0mloop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mserve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msockets\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msockets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m     \u001b[1;32masync\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mserve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msockets\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\asyncio\\base_events.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m    590\u001b[0m         \"\"\"\n\u001b[0;32m    591\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_running\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    593\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m         \u001b[0mnew_task\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mfutures\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfuture\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\asyncio\\base_events.py\u001b[0m in \u001b[0;36m_check_running\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    550\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_check_running\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    551\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 552\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'This event loop is already running'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    553\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mevents\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_running_loop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    554\u001b[0m             raise RuntimeError(\n",
      "\u001b[1;31mRuntimeError\u001b[0m: This event loop is already running"
     ]
    }
   ],
   "source": [
    "import uvicorn\n",
    "from fastapi import FastAPI\n",
    "import joblib,os\n",
    "\n",
    "app = FastAPI()\n",
    "\n",
    "#pkl\n",
    "phish_model = open('phishing.pkl','rb')\n",
    "phish_model_ls = joblib.load(phish_model)\n",
    "\n",
    "# ML Aspect\n",
    "@app.get('/predict/{feature}')\n",
    "async def predict(features):\n",
    "\tX_predict = []\n",
    "\tX_predict.append(str(features))\n",
    "\ty_Predict = phish_model_ls.predict(X_predict)\n",
    "\tif y_Predict == 'bad':\n",
    "\t\tresult = \"This is a Phishing Site\"\n",
    "\telse:\n",
    "\t\tresult = \"This is not a Phishing Site\"\n",
    "\n",
    "\treturn (features, result)\n",
    "if __name__ == '__main__':\n",
    "\tuvicorn.run(app,host=\"127.0.0.1\",port=8000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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