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app.py
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app.py
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import streamlit as st
from urllib.parse import urlparse,urlencode
import ipaddress
import re
from bs4 import BeautifulSoup
import whois
import urllib.request
import requests
import socket
import time
import pickle
import numpy as np
from googlesearch import search
from datetime import date, datetime
from dateutil.parser import parse as date_parse
import json
import base64
import csv
import urllib.request
import pandas as pd
import urllib.request
def diff_month(d1, d2):
return (d1.year - d2.year) * 12 + d1.month - d2.month
def ExtractFeatures(url):
data_set = []
if not re.match(r"^https?", url):
url = "http://" + url
try:
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
except:
response = ""
soup = -999
domain = re.findall(r"://([^/]+)/?", url)[0]
if re.match(r"^www.", domain):
domain = domain.replace("www.", "")
whois_response = whois.whois(domain)
rank_checker_response = requests.post("https://www.checkpagerank.net/index.php", {
"name": domain
})
try:
global_rank = int(re.findall(
r"Global Rank: ([0-9]+)", rank_checker_response.text)[0])
except:
global_rank = -1
# 1.having_IP_Address
try:
ipaddress.ip_address(url)
data_set.append(-1)
except:
data_set.append(1)
# 2.URL_Length
if len(url) < 54:
data_set.append(1)
elif len(url) >= 54 and len(url) <= 75:
data_set.append(0)
else:
data_set.append(-1)
# 3.Shortining_Service
match = re.search('bit\.ly|goo\.gl|shorte\.st|go2l\.ink|x\.co|ow\.ly|t\.co|tinyurl|tr\.im|is\.gd|cli\.gs|'
'yfrog\.com|migre\.me|ff\.im|tiny\.cc|url4\.eu|twit\.ac|su\.pr|twurl\.nl|snipurl\.com|'
'short\.to|BudURL\.com|ping\.fm|post\.ly|Just\.as|bkite\.com|snipr\.com|fic\.kr|loopt\.us|'
'doiop\.com|short\.ie|kl\.am|wp\.me|rubyurl\.com|om\.ly|to\.ly|bit\.do|t\.co|lnkd\.in|'
'db\.tt|qr\.ae|adf\.ly|goo\.gl|bitly\.com|cur\.lv|tinyurl\.com|ow\.ly|bit\.ly|ity\.im|'
'q\.gs|is\.gd|po\.st|bc\.vc|twitthis\.com|u\.to|j\.mp|buzurl\.com|cutt\.us|u\.bb|yourls\.org|'
'x\.co|prettylinkpro\.com|scrnch\.me|filoops\.info|vzturl\.com|qr\.net|1url\.com|tweez\.me|v\.gd|tr\.im|link\.zip\.net', url)
if match:
data_set.append(-1)
else:
data_set.append(1)
# 4.having_At_Symbol
if re.findall("@", url):
data_set.append(-1)
else:
data_set.append(1)
# 5.double_slash_redirecting
list = [x.start(0) for x in re.finditer('//', url)]
if list[len(list)-1] > 6:
data_set.append(-1)
else:
data_set.append(1)
# 6.Prefix_Suffix
if re.findall(r"https?://[^\-]+-[^\-]+/", url):
data_set.append(-1)
else:
data_set.append(1)
# 7.having_Sub_Domain
if len(re.findall("\.", url)) == 1:
data_set.append(1)
elif len(re.findall("\.", url)) == 2:
data_set.append(0)
else:
data_set.append(-1)
# 8.SSLfinal_State
try:
if response.text:
data_set.append(1)
except:
data_set.append(-1)
# 9.Domain_registeration_length
expiration_date = whois_response.expiration_date
registration_length = 0
try:
expiration_date = min(expiration_date)
today = time.strftime('%Y-%m-%d')
today = datetime.strptime(today, '%Y-%m-%d')
registration_length = abs((expiration_date - today).days)
if registration_length / 365 <= 1:
data_set.append(-1)
else:
data_set.append(1)
except:
data_set.append(-1)
# 10.Favicon
if soup == -999:
data_set.append(-1)
else:
try:
for head in soup.find_all('head'):
for head.link in soup.find_all('link', href=True):
dots = [x.start(0)
for x in re.finditer('\.', head.link['href'])]
if url in head.link['href'] or len(dots) == 1 or domain in head.link['href']:
data_set.append(1)
raise StopIteration
else:
data_set.append(-1)
raise StopIteration
except StopIteration:
pass
# 11. port
try:
port = domain.split(":")[1]
if port:
data_set.append(-1)
else:
data_set.append(1)
except:
data_set.append(1)
# 12. HTTPS_token
if re.findall(r"^https://", url):
data_set.append(1)
else:
data_set.append(-1)
# 13. Request_URL
i = 0
success = 0
if soup == -999:
data_set.append(-1)
else:
for img in soup.find_all('img', src=True):
dots = [x.start(0) for x in re.finditer('\.', img['src'])]
if url in img['src'] or domain in img['src'] or len(dots) == 1:
success = success + 1
i = i+1
for audio in soup.find_all('audio', src=True):
dots = [x.start(0) for x in re.finditer('\.', audio['src'])]
if url in audio['src'] or domain in audio['src'] or len(dots) == 1:
success = success + 1
i = i+1
for embed in soup.find_all('embed', src=True):
dots = [x.start(0) for x in re.finditer('\.', embed['src'])]
if url in embed['src'] or domain in embed['src'] or len(dots) == 1:
success = success + 1
i = i+1
for iframe in soup.find_all('iframe', src=True):
dots = [x.start(0) for x in re.finditer('\.', iframe['src'])]
if url in iframe['src'] or domain in iframe['src'] or len(dots) == 1:
success = success + 1
i = i+1
try:
percentage = success/float(i) * 100
if percentage < 22.0:
data_set.append(1)
elif((percentage >= 22.0) and (percentage < 61.0)):
data_set.append(0)
else:
data_set.append(-1)
except:
data_set.append(1)
# 14. URL_of_Anchor
percentage = 0
i = 0
unsafe = 0
if soup == -999:
data_set.append(-1)
else:
for a in soup.find_all('a', href=True):
# 2nd condition was 'JavaScript ::void(0)' but we put JavaScript because the space between javascript and :: might not be
# there in the actual a['href']
if "#" in a['href'] or "javascript" in a['href'].lower() or "mailto" in a['href'].lower() or not (url in a['href'] or domain in a['href']):
unsafe = unsafe + 1
i = i + 1
try:
percentage = unsafe / float(i) * 100
except:
data_set.append(1)
if percentage < 31.0:
data_set.append(1)
elif ((percentage >= 31.0) and (percentage < 67.0)):
data_set.append(0)
else:
data_set.append(-1)
# 15. Links_in_tags
i = 0
success = 0
if soup == -999:
data_set.append(-1)
data_set.append(0)
else:
for link in soup.find_all('link', href=True):
dots = [x.start(0) for x in re.finditer('\.', link['href'])]
if url in link['href'] or domain in link['href'] or len(dots) == 1:
success = success + 1
i = i+1
for script in soup.find_all('script', src=True):
dots = [x.start(0) for x in re.finditer('\.', script['src'])]
if url in script['src'] or domain in script['src'] or len(dots) == 1:
success = success + 1
i = i+1
try:
percentage = success / float(i) * 100
except:
data_set.append(1)
if percentage < 17.0:
data_set.append(1)
elif((percentage >= 17.0) and (percentage < 81.0)):
data_set.append(0)
else:
data_set.append(-1)
# 16. SFH
if len(soup.find_all('form', action=True))==0:
data_set.append(1)
else :
for form in soup.find_all('form', action=True):
if form['action'] == "" or form['action'] == "about:blank":
data_set.append(-1)
break
elif url not in form['action'] and domain not in form['action']:
data_set.append(0)
break
else:
data_set.append(1)
break
# 17. Submitting_to_email
if response == "":
data_set.append(-1)
else:
if re.findall(r"[mail\(\)|mailto:?]", response.text):
data_set.append(-1)
else:
data_set.append(1)
# 18. Abnormal_URL
if response == "":
data_set.append(-1)
else:
if response.text == whois_response:
data_set.append(1)
else:
data_set.append(-1)
# 19. Redirect
if response == "":
data_set.append(-1)
else:
if len(response.history) <= 1:
data_set.append(-1)
elif len(response.history) <= 4:
data_set.append(0)
else:
data_set.append(1)
# 20. on_mouseover
if response == "":
data_set.append(-1)
else:
if re.findall("<script>.+onmouseover.+</script>", response.text):
data_set.append(1)
else:
data_set.append(-1)
# 21. RightClick
if response == "":
data_set.append(-1)
else:
if re.findall(r"event.button ?== ?2", response.text):
data_set.append(1)
else:
data_set.append(-1)
# 22. popUpWidnow
if response == "":
data_set.append(-1)
else:
if re.findall(r"alert\(", response.text):
data_set.append(1)
else:
data_set.append(-1)
# 23. Iframe
if response == "":
data_set.append(-1)
else:
if re.findall(r"[<iframe>|<frameBorder>]", response.text):
data_set.append(1)
else:
data_set.append(-1)
# 24. age_of_domain
if response == "":
data_set.append(-1)
else:
try:
registration_date = re.findall(
r'Registration Date:</div><div class="df-value">([^<]+)</div>', whois_response.text)[0]
if diff_month(date.today(), date_parse(registration_date)) >= 6:
data_set.append(-1)
else:
data_set.append(1)
except:
data_set.append(1)
# 25. DNSRecord
dns = 1
try:
d = whois.whois(domain)
except:
dns = -1
if dns == -1:
data_set.append(-1)
else:
if registration_length / 365 <= 1:
data_set.append(-1)
else:
data_set.append(1)
# 26. web_traffic
try:
rank = BeautifulSoup(urllib.request.urlopen(
"http://data.alexa.com/data?cli=10&dat=s&url=" + url).read(), "xml").find("REACH")['RANK']
rank = int(rank)
if (rank < 100000):
data_set.append(1)
else:
data_set.append(0)
except :
data_set.append(-1)
# 27. Page_Rank
try:
if global_rank > 0 and global_rank < 100000:
data_set.append(-1)
else:
data_set.append(1)
except:
data_set.append(1)
# 28. Google_Index
site = search(url, 5)
if site:
data_set.append(1)
else:
data_set.append(-1)
# 29. Links_pointing_to_page
if response == "":
data_set.append(-1)
else:
number_of_links = len(re.findall(r"<a href=", response.text))
if number_of_links == 0:
data_set.append(1)
elif number_of_links <= 2:
data_set.append(0)
else:
data_set.append(-1)
# 30. Statistical_report
url_match = re.search(
'at\.ua|usa\.cc|baltazarpresentes\.com\.br|pe\.hu|esy\.es|hol\.es|sweddy\.com|myjino\.ru|96\.lt|ow\.ly', url)
try:
ip_address = socket.gethostbyname(domain)
ip_match = re.search('146\.112\.61\.108|213\.174\.157\.151|121\.50\.168\.88|192\.185\.217\.116|78\.46\.211\.158|181\.174\.165\.13|46\.242\.145\.103|121\.50\.168\.40|83\.125\.22\.219|46\.242\.145\.98|'
'107\.151\.148\.44|107\.151\.148\.107|64\.70\.19\.203|199\.184\.144\.27|107\.151\.148\.108|107\.151\.148\.109|119\.28\.52\.61|54\.83\.43\.69|52\.69\.166\.231|216\.58\.192\.225|'
'118\.184\.25\.86|67\.208\.74\.71|23\.253\.126\.58|104\.239\.157\.210|175\.126\.123\.219|141\.8\.224\.221|10\.10\.10\.10|43\.229\.108\.32|103\.232\.215\.140|69\.172\.201\.153|'
'216\.218\.185\.162|54\.225\.104\.146|103\.243\.24\.98|199\.59\.243\.120|31\.170\.160\.61|213\.19\.128\.77|62\.113\.226\.131|208\.100\.26\.234|195\.16\.127\.102|195\.16\.127\.157|'
'34\.196\.13\.28|103\.224\.212\.222|172\.217\.4\.225|54\.72\.9\.51|192\.64\.147\.141|198\.200\.56\.183|23\.253\.164\.103|52\.48\.191\.26|52\.214\.197\.72|87\.98\.255\.18|209\.99\.17\.27|'
'216\.38\.62\.18|104\.130\.124\.96|47\.89\.58\.141|78\.46\.211\.158|54\.86\.225\.156|54\.82\.156\.19|37\.157\.192\.102|204\.11\.56\.48|110\.34\.231\.42', ip_address)
if url_match:
data_set.append(-1)
elif ip_match:
data_set.append(-1)
else:
data_set.append(1)
except:
print('Connection problem. Please check your internet connection')
return data_set
st.title("Phishing Websites Detection")
# DB Management
import sqlite3
conn = sqlite3.connect('data.db')
c = conn.cursor()
def create_usertable():
c.execute('CREATE TABLE IF NOT EXISTS userstable(username TEXT,password TEXT)')
def add_userdata(username,password):
c.execute('INSERT INTO userstable(username,password) VALUES (?,?)',(username,password))
conn.commit()
def login_user(username,password):
c.execute('SELECT * FROM userstable WHERE username =? AND password = ?',(username,password))
data = c.fetchall()
return data
# Security
#passlib,hashlib,bcrypt,scrypt
import hashlib
def make_hashes(password):
return hashlib.sha256(str.encode(password)).hexdigest()
def check_hashes(password,hashed_text):
if make_hashes(password) == hashed_text:
return hashed_text
return False
def view_all_users():
c.execute('SELECT * FROM userstable')
data = c.fetchall()
return data
def loginSys():
menu = ["Home", "Login", "Signup"]
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Home":
st.subheader("Welcome")
st.markdown("**Login or Signup to Access the System**")
elif choice == "Login":
st.subheader("Home Page")
username = st.sidebar.text_input("Username")
password = st.sidebar.text_input("Password", type = 'password')
if st.sidebar.checkbox("Login"):
create_usertable()
hashed_pass = make_hashes(password)
result = login_user(username, check_hashes(password,hashed_pass))
if result:
if username == "noura":
st.success("Logged in as Adminstrator")
menu_task = ["Analytics", "Add New Data", "Classify URL"]
task = st.selectbox("Task", menu_task)
if task == "Classify URL":
st.subheader("**Enter URL To Classify:**")
user_input = st.text_input("Input URL")
features = ExtractFeatures(user_input)
features = np.reshape(features, (-1,1)).T
classify(user_input, features)
elif task == "Analytics":
st.markdown("** Verified Phishing Websites **")
st.dataframe(df)
st.markdown("**Current Users**")
user_result = view_all_users()
clean_db = pd.DataFrame(user_result, columns =['Username', 'Password'] )
st.dataframe(clean_db)
elif task == "Add New Data":
data = st.file_uploader("Choose a CSV File")
data = pd.DataFrame(data)
data.to_csv("/home/n/Desktop/The Goal/SE-Project-mat/verified_phishing_sites.csv", mode = 'a', header = False)
else:
st.success("Logged in as {}".format(username))
st.markdown("**URL Detection**")
user_input = st.text_input("Input URL")
features = ExtractFeatures(user_input)
features = np.reshape(features, (-1,1))
classify(user_input, features)
st.button("Generate Report")
# Create another option to generate report
else:
st.error("Password or username is wrong, please try again!")
elif choice == "Signup":
st.subheader("Create Account")
new_user = st.text_input("Username")
new_pass = st.text_input("Password", type = 'password')
if st.button("Signup"):
create_usertable()
add_userdata(new_user, make_hashes(new_pass))
st.success("You Have Successfully Created a Valid Account!")
st.info("Go to Login Menu to Login")
df = pd.read_csv("verified_phishing_sites.csv")
df.drop(df.index[-1], inplace = True)
#df.drop('0', axis =1 , inplace = True)
from io import BytesIO, StringIO
@st.cache
def load_model(url):
# object = pd.read_pickle(url2)
# with open("https://github.com/noura-na/phishing-websites-detection-app/releases/download/tag/model/model.sav", 'rb') as res:
# model = pickle.load(filename)
# from urllib.request import urlopen
# import gzip
# import cloudpickle as cp
# #with gzip.open('test.pklz', 'rb') as ifp: #urlopen
# loaded_pickle_object = cp.load(gzip.open("https://drive.google.com/uc?export=download&id=1cKb_kPBnu8meKEmJy5ooux6sg5oQZwaH"))
# return loaded_pickle_object
# modelLink = url
model = requests.get(url).content
return model
model = load_model("https://drive.google.com/uc?export=download&id=1cKb_kPBnu8meKEmJy5ooux6sg5oQZwaH")
model = BytesIO(model)
def predict_prob(number):
return [number[0],1-number[0]]
def makePrediction(features, url):
#probability = np.array(list(map(predict_prob, model.predict(features))))
#probability = model.predict_proba(features)
output = model.predict(features)
if output[0] == 1:
output = "Legitimate"
st.success(output)
else:
df.loc[len(df.index)] = url
df_new = df.copy()
df_new.to_csv("verified_phishing_sites_updated.csv", index = False)
output = "Phishing"
st.warning(output)
st.markdown('**Predicted Probability**')
#st.bar_chart(probability)
def checkinDB(user_input):
if df[df['url'].str.contains(user_input)].count()[0]:
st.warning("Phishing")
return -1
return 0
def classify(url, features):
if checkinDB(url) != -1:
makePrediction(features, url)
loginSys()