feature_extractor (1).py

import pandas as pd 
import numpy as np

import rf_model


class feature_extractor:

    def __init__(self,url:str):
        self.input_url = url

    def long_url(self,l):
        """This function is defined in order to differntiate website based on the length of the URL"""
        l= str(l)
        if len(l) < 54:
            return 0
        elif len(l) >= 54 and len(l) <= 75:
            return 2
        return 1

    def have_at_symbol(self,l):
        """This function is used to check whether the URL contains @ symbol or not"""
        if "@" in str(l):
            return 1
        return 0

    def redirection(self,l):
        """If the url has symbol(//) after protocol then such URL is to be classified as phishing """
        if "//" in str(l):
            return 1
        return 0

    def prefix_suffix_seperation(self,l):
        """seprate prefix and suffix"""
        if '-' in str(l):
            return 1
        return 0

    def sub_domains(self,l):
        """check the subdomains"""
        l= str(l)
        if l.count('.') < 3:
            return 0
        elif l.count('.') == 3:
            return 2
        return 1


    def extract(self):
        print("in script 2")
        input_data = [{"URL":self.input_url}]
        print('input taken')
        temp_df = pd.DataFrame(input_data)
        print("dataframe created")
        #expand argument in the split method will give you a new column
        seperation_of_protocol = temp_df['URL'].str.split("://",expand = True)
        print("step 1 done")
        #split(seperator,no of splits according to seperator(delimiter),expand)
        seperation_domain_name = seperation_of_protocol[1].str.split("/",1,expand = True)
        print("step 2 done")
        #renaming columns of data frame
        seperation_domain_name.columns=["domain_name","address"]
        print("step 3 done")
        #Concatenation of data frames
        splitted_data = pd.concat([seperation_of_protocol[0],seperation_domain_name],axis=1)
        print("step 4 done")

        splitted_data.columns = ['protocol','domain_name','address']
        print("step 5 done")

        #splitted_data['is_phished'] = pd.Series(temp_df['Target'], index=splitted_data.index)
        #print("step 6 done")

        """feature extraction starts here"""
        #Applying the above defined function in order to divide the websites into 3 categories
        splitted_data['long_url'] = temp_df['URL'].apply(self.long_url)
        print("feature extra 1")
        splitted_data['having_@_symbol'] = temp_df['URL'].apply(self.have_at_symbol)
        print("feature extra 2")
        splitted_data['redirection_//_symbol'] = seperation_of_protocol[1].apply(self.redirection)
        print("feature extra 3")
        splitted_data['prefix_suffix_seperation'] = seperation_domain_name['domain_name'].apply(self.prefix_suffix_seperation)
        print("feature extra 4")
        splitted_data['sub_domains'] = splitted_data['domain_name'].apply(self.sub_domains)
        print("feature extra 5")
        #splitted_data.to_csv(r'dataset3.csv',header= True)

        

        return rf_model.predictor(splitted_data)