rf.py

import pandas as pd
import numpy as np
from pandas import ExcelWriter
from pandas import ExcelFile
import re
from sklearn.metrics import confusion_matrix 
# from sklearn.cross_validation import train_test_split 
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier 
from sklearn.metrics import accuracy_score 
from sklearn.metrics import classification_report 
from sklearn.naive_bayes import GaussianNB 
from sklearn import metrics 
#importing wx files
import wx
#import the newly created GUI file

from sklearn import svm
import webbrowser
from sklearn.ensemble import RandomForestClassifier
import seaborn as sns
from yellowbrick.classifier import ClassificationReport
from sklearn.metrics import confusion_matrix,accuracy_score,roc_auc_score,roc_curve
from sklearn import preprocessing



#extract single feature
def extract_feature_usertest(url):
    
    
    #length of url
    l_url=len(url)
    if(l_url > 54):
        length_of_url = 1
    else:
        length_of_url = 0

    
    #url has http
    if (("http://" in url) or ("https://" in url)):
        http_has = 1
    else:
        http_has = 0

    #url has suspicious char
    if (("@" in url) or ("//" in url)):
        suspicious_char = 1
    else:
        suspicious_char = 0


    #prefix or suffix
    if ("-" in url):
        prefix_suffix = 1
    else:
        prefix_suffix = 0

    #no of dots
    if ("." in url):
        count = len(url.split('.'))-1
        if (count > 5):
            dots = 0
        else:
            dots = 1
    else:
        dots = 0
    
    #no of slash
    if ("/" in url):
        count = len(url.split('/'))-1
        if (count > 5):
            slash = 0
        else:
            slash = 1
    else:
        slash = 0

    #url has phishing terms
    #("secure" in url) or ("secure" in url) or ("websrc" in url) or ("ebaysapi" in url) or ("signin" in url) or ("banking" in url) or ("confirm" in url) or ("login" in url)
    if (("secure" in url) or ("secure" in url) or ("websrc" in url) or ("ebaysapi" in url) or ("signin" in url) or ("banking" in url) or ("confirm" in url) or ("login" in url)):
        phis_term = 1
    else:
        phis_term = 0
    
    #length of subdomain
    it = url.index("//") + 2
    j = url.index(".")
    c = j - it;
    if (c > 5):
        sub_domain = 0
    else:
        sub_domain = 1
    
    #url contains ip address
    if re.match("\b(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\b",url):
        ip_contain = 1
    else:
        ip_contain = 0
    
    
    return length_of_url,http_has,suspicious_char,prefix_suffix,dots,slash,phis_term,sub_domain,ip_contain







#extract testing feature
def extract_feature_test(url,output):
    
    
    #length of url
    l_url=len(url)
    if(l_url > 54):
        length_of_url = 1
    else:
        length_of_url = 0

    
    #url has http
    if (("http://" in url) or ("https://" in url)):
        http_has = 1
    else:
        http_has = 0

    #url has suspicious char
    if (("@" in url) or ("//" in url)):
        suspicious_char = 1
    else:
        suspicious_char = 0


    #prefix or suffix
    if ("-" in url):
        prefix_suffix = 1
    else:
        prefix_suffix = 0

    #no of dots
    if ("." in url):
        count = len(url.split('.'))-1
        if (count > 5):
            dots = 0
        else:
            dots = 1
    else:
        dots = 0
    
    #no of slash
    if ("/" in url):
        count = len(url.split('/'))-1
        if (count > 5):
            slash = 0
        else:
            slash = 1
    else:
        slash = 0

    #url has phishing terms
    #("secure" in url) or ("secure" in url) or ("websrc" in url) or ("ebaysapi" in url) or ("signin" in url) or ("banking" in url) or ("confirm" in url) or ("login" in url)
    if (("secure" in url) or ("secure" in url) or ("websrc" in url) or ("ebaysapi" in url) or ("signin" in url) or ("banking" in url) or ("confirm" in url) or ("login" in url)):
        phis_term = 1
    else:
        phis_term = 0
    
    #length of subdomain
    it = url.index("//") + 2
    j = url.index(".")
    c = j - it;
    if (c > 5):
        sub_domain = 0
    else:
        sub_domain = 1
    
    #url contains ip address
    if re.match("\b(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\b",url):
        ip_contain = 1
    else:
        ip_contain = 0
    
    #output
    yn = output
    
    return yn,length_of_url,http_has,suspicious_char,prefix_suffix,dots,slash,phis_term,sub_domain,ip_contain
#extract training feature
def extract_feature_train(url,output):
    
    
    #length of url
    l_url=len(url)
    if(l_url > 54):
        length_of_url = 1
    else:
        length_of_url = 0

    
    #url has http
    if (("http://" in url) or ("https://" in url)):
        http_has = 1
    else:
        http_has = 0

    #url has suspicious char
    if (("@" in url) or ("//" in url)):
        suspicious_char = 1
    else:
        suspicious_char = 0


    #prefix or suffix
    if ("-" in url):
        prefix_suffix = 1
    else:
        prefix_suffix = 0

    #no of dots
    if ("." in url):
        count = len(url.split('.'))-1
        if (count > 5):
            dots = 0
        else:
            dots = 1
    else:
        dots = 0
    
    #no of slash
    if ("/" in url):
        count = len(url.split('/'))-1
        if (count > 5):
            slash = 0
        else:
            slash = 1
    else:
        slash = 0

    #url has phishing terms
    #("secure" in url) or ("secure" in url) or ("websrc" in url) or ("ebaysapi" in url) or ("signin" in url) or ("banking" in url) or ("confirm" in url) or ("login" in url)
    if (("secure" in url) or ("secure" in url) or ("websrc" in url) or ("ebaysapi" in url) or ("signin" in url) or ("banking" in url) or ("confirm" in url) or ("login" in url)):
        phis_term = 1
    else:
        phis_term = 0
    
    #length of subdomain
    it = url.index("//") + 2
    j = url.index(".")
    c = j - it;
    if (c > 5):
        sub_domain = 0
    else:
        sub_domain = 1
    
    #url contains ip address
    if re.match("\b(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})\b",url):
        ip_contain = 1
    else:
        ip_contain = 0
    
    #output
    yn = output

   
        
    return yn,length_of_url,http_has,suspicious_char,prefix_suffix,dots,slash,phis_term,sub_domain,ip_contain
#import train data
def importdata_train(): 
    balance_data = pd.read_csv('feature_train.csv',sep= ',', header = 1,usecols=range(1,11),encoding='utf-8') 
      
      
    # Printing the dataswet shape 
    print ("Dataset Lenght: ", len(balance_data)) 
    print ("Dataset Shape: ", balance_data.shape) 
      
    # Printing the dataset obseravtions 
    print ("Dataset: ",balance_data.head()) 
    return balance_data 
#import test data
def importdata_test(): 
    balance_data = pd.read_csv('feature_test.csv',sep= ',', header = 1,usecols=range(1,11),encoding='utf-8') 
      
      
    # Printing the dataswet shape 
    print ("Dataset Lenght: ", len(balance_data)) 
    print ("Dataset Shape: ", balance_data.shape) 
      
    # Printing the dataset obseravtions 
    print ("Dataset: ",balance_data.head()) 
    return balance_data 
#split data into train and test
def splitdataset(balance_data): 
  
    # Seperating the target variable 
    X = balance_data.values[:, 1:10]
    Y = balance_data.values[:, 0] 
  
    # Spliting the dataset into train and test 
    #X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state = 100) 
      
    return X, Y
# Function to perform training with entropy. 
def tarin_using_entropy(X_train, y_train): 
  
    # Decision tree with entropy 
    clf_entropy = DecisionTreeClassifier( 
            criterion = "entropy", random_state = 100, 
            max_depth = 2, min_samples_leaf = 10) 
  
    # Performing training 
    clf_entropy.fit(X_train, y_train) 
    return clf_entropy 
# Function to make predictions 
def prediction(X_test, clf_object): 
  
    # Predicton on test with giniIndex 
    y_pred = clf_object.predict(X_test) 
    #print("Predicted values:") 
    #print(y_pred) 
    return y_pred 
# Function to calculate accuracy 
def cal_accuracy(y_test, y_pred): 
      
    print("Confusion Matrix: ", 
        confusion_matrix(y_test, y_pred)) 
      
    print ("Accuracy : ", 
    accuracy_score(y_test,y_pred)*100) 
      
    print("Report : ", 
    classification_report(y_test, y_pred))

    return accuracy_score(y_test,y_pred)*100




#main funcation
def main():
    excel_file= 'training.xlsx'
    df=pd.DataFrame(pd.read_excel(excel_file))
    excel_file_test= 'test1.xlsx'
    df1=pd.DataFrame(pd.read_excel(excel_file_test))

    a=[]
    b=[]
    a1=[]
    b1=[]
    for url in df['url']:
        a.append(url)

    for output in df['phishing']:
        b.append(output)

    for url1 in df1['url']:
        a1.append(url1)

    for output in df1['result']:
        b1.append(output)

    c=[]
    d=[]
    for url1,output1 in zip(a,b):       
        url=url1
        output=output1
        c.append(extract_feature_train(url,output))

    for url1,output1 in zip(a1,b1):           
        url=url1
        output=output1
        d.append(extract_feature_test(url,output))



    df=pd.DataFrame(c,columns=['r','length_of_url','http_has','suspicious_char','prefix_suffix','dots','slash','phis_term','sub_domain','ip_contain'])

    df.to_csv('feature_train.csv', sep=',', encoding='utf-8')

    df_test=pd.DataFrame(d,columns=['r','length_of_url','http_has','suspicious_char','prefix_suffix','dots','slash','phis_term','sub_domain','ip_contain'])

    df_test.to_csv('feature_test.csv', sep=',', encoding='utf-8')  
    
    data_train=importdata_train()

    data_test=importdata_test()

    X, Y = splitdataset(data_train)
    X1, Y1 = splitdataset(data_test)

    Y=np.where(Y=='yes','1', Y) 
    Y=np.where(Y=='no','0', Y) 
    Y1=np.where(Y1=='yes','1', Y1) 
    Y1=np.where(Y1=='no','0', Y1) 



    
    model=RandomForestClassifier()
    model.fit(X,Y)




    class MainFrame ( wx.Frame ):

        def __init__( self, parent ):
            wx.Frame.__init__ ( self, parent, id = wx.ID_ANY, title = wx.EmptyString, pos = wx.DefaultPosition, size = wx.Size( 500,300 ), style = wx.DEFAULT_FRAME_STYLE|wx.TAB_TRAVERSAL )

            self.SetSizeHintsSz( wx.DefaultSize, wx.DefaultSize )

            bSizer3 = wx.BoxSizer( wx.VERTICAL )

            self.m_staticText2 = wx.StaticText( self, wx.ID_ANY, u"Enter URL", wx.DefaultPosition, wx.DefaultSize, 0 )
            self.m_staticText2.Wrap( -1 )
            bSizer3.Add( self.m_staticText2, 0, wx.ALL, 5 )
        
            self.text1 = wx.TextCtrl( self, wx.ID_ANY, wx.EmptyString, wx.DefaultPosition, wx.DefaultSize, 0 )
            bSizer3.Add( self.text1, 0, wx.ALL|wx.EXPAND, 5 )

            self.predictButton = wx.Button( self, wx.ID_ANY, u"Predict_Random Forest", wx.DefaultPosition, wx.DefaultSize, 0 )
            bSizer3.Add( self.predictButton, 0, wx.ALL|wx.EXPAND, 5 )
        

        
            # self.label1 = wx.StaticText( self, wx.ID_ANY, u"Result", wx.DefaultPosition, wx.DefaultSize, 0 )
            # self.label1.Wrap( -1 )
            # bSizer3.Add( self.label1, 0, wx.ALL|wx.ALIGN_CENTER_HORIZONTAL, 5 )
        
            # self.text2 = wx.TextCtrl( self, wx.ID_ANY, wx.EmptyString, wx.DefaultPosition, wx.DefaultSize, 0 )
            # bSizer3.Add( self.text2, 0, wx.RIGHT|wx.EXPAND, 5 )
        
        
            self.SetSizer( bSizer3 )
            self.Layout()
        
            self.Centre( wx.BOTH )
        
            # Connect Events
            self.predictButton.Bind( wx.EVT_BUTTON, self.click )


        def __del__( self ):
            pass


        # Virtual event handlers, overide them in your derived class



        #XGBOOST
        def click( self, event ):
            try:
                url = self.text1.GetValue()
                e=np.array([extract_feature_usertest(url)])
                userpredict1 = model.predict(e.reshape(1,-1)) 
                if(userpredict1[0]=='0'):
                    # self.text2.SetValue(str("Legitimate"))
                    print('Legitimate')
                    class MyDialog1 ( wx.Dialog ):

                        def __init__( self, parent ):
                            wx.Dialog.__init__ ( self, parent, id = wx.ID_ANY, title = wx.EmptyString, pos = wx.DefaultPosition, size = wx.Size( 159,114 ), style = wx.DEFAULT_DIALOG_STYLE )
        
                            self.SetSizeHintsSz( wx.DefaultSize, wx.DefaultSize )
        
                            sbSizer1 = wx.StaticBoxSizer( wx.StaticBox( self, wx.ID_ANY, u"POP-UP" ), wx.VERTICAL )
        
                            self.m_staticText1 = wx.StaticText( sbSizer1.GetStaticBox(), wx.ID_ANY, u"LEGITIMATE", wx.DefaultPosition, wx.DefaultSize, 0 )
                            self.m_staticText1.Wrap( -1 )
                            sbSizer1.Add( self.m_staticText1, 0, wx.ALL|wx.ALIGN_CENTER_HORIZONTAL, 5 )

                            self.SetSizer( sbSizer1 )
                            self.Layout()
        
                            self.Centre( wx.BOTH )

                    app3 = wx.App(False)
                    frame = MyDialog1(None)
                    frame.Show(True)
                    webbrowser.open(url)
                    app3.MainLoop()

                else:
                    class MyDialog1 ( wx.Dialog ):

                        def __init__( self, parent ):
                            wx.Dialog.__init__ ( self, parent, id = wx.ID_ANY, title = wx.EmptyString, pos = wx.DefaultPosition, size = wx.Size( 200,150), style = wx.DEFAULT_DIALOG_STYLE )
        
                            self.SetSizeHintsSz( wx.DefaultSize, wx.DefaultSize )
        
                            sbSizer1 = wx.StaticBoxSizer( wx.StaticBox( self, wx.ID_ANY, u"Error" ), wx.VERTICAL )
        
                            self.m_staticText1 = wx.StaticText( sbSizer1.GetStaticBox(), wx.ID_ANY, u"PHISING", wx.DefaultPosition, wx.DefaultSize, 0 )
                            self.m_staticText1.Wrap( -1 )
                            sbSizer1.Add( self.m_staticText1, 0, wx.ALL|wx.ALIGN_CENTER_HORIZONTAL, 5 )


        
                            self.SetSizer( sbSizer1 )
                            self.Layout()
        
                            self.Centre( wx.BOTH )

                            def __del__( self ):
                                pass


    # Virtual event handlers, overide them in your derived class
                            def click( self, event ):
                                event.Skip()
                    app2 = wx.App(False)
                    frame = MyDialog1(None)
                    frame.Show(True)
                    app2.MainLoop() 

                    # self.text2.SetValue(str("Phising"))
                    # print('Phising')
            except Exception:
                print ('error')



    app1 = wx.App(False)
    frame = MainFrame(None)
    frame.Show(True)
    app1.MainLoop() 






  
if __name__== "__main__":
  main()