Abstract :

In recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyber world. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus web pages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the “zero-day” attacks.

Phishing costs Internet user’s lots of dollars per year. It refers to exploiting weakness on the user side, which is vulnerable to such attacks. The phishing problem is huge and there does not exist only one solution to minimize all vulnerabilities effectively, thus multiple techniques are implemented.

So we proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree , XGB Classifier and  Naïve Bayes to predict Phishing Website based on different parameters like extracted by the website link entered by the user in the front end.

 

Objective:

The main aim of this project to predict the Phishing Webiste using machine learning techniques and algorithms like like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree, XGB Classifier and  Naïve Bayes based on different parameters different parameters like extracted by the website link entered by the user in the front end.

Problem Statement

Phishing costs Internet user’s lots of dollars per year. It refers to exploiting weakness on the user side, which is vulnerable to such attacks. The phishing problem is huge and there does not exist only one solution to minimize all vulnerabilities effectively, thus multiple techniques are implemented. Detection of Phishing Website plays an important role to avoiding loss of personal data of the user by hackers using these phishing websites.

Proposed System

We proposed a system with the help of machine learning techniques and algorithms like Logistic Regression, KNN, SVC, Random Forest ,Decision Tree , XGB Classifier and  Naïve Bayes to predict Phishing Website based on different parameters like extracted by the website link entered by the user in the front end.

System Requirements:

Hardware Requirement:-

•      System :Pentium IV 2.4 GHz. 

•      Hard Disk : 500 GB.

•      Ram : 4 GB.

•      Any desktop / Laptop system with above configuration or higher level.

 

Software Requirements:-

•      Operating system : Windows XP / 7

•      Coding Language :Python, HTML

•      Version       :Python 3.6.8

•      IDE             : Python 3.6.8 IDLE

•      ML Packages    :Numpy, Pandas ,Sklearn, Matplotlib, Seaborn, Flask, PymySql.

•      ML Algorithms: Logistic Regression, Random Forest Classifier, KNN, SVC, Naïve Bayes, Decision Tree Classifier and XGB Classifier.

•      Other Requirements : Notepad, XAMPP Control Panel

 

Methodology

 

·         A Phishing Website Dataset is taken.

·         The dataset is loaded and preprocessed with various machine learning techniques.

·         The preprocessed data is divided as training and testing data.

·         The prediction model is built using machine learning algorithms like  Logistic Regression, KNN, SVC, Random Forest ,Decision Tree, Naïve Bayes and XGB Classifier.

·         The model is trained using training dataset and once the model has been trained successfully it has to be tested.

·         The trained model is tested using testing dataset and accuracy is calculated.

·         The algorithm which gives the best accuracy is taken as our final prediction model.

·         The finalized model is converted into pickle model (binary format data) and saved.

·         A Front End is developed with the help of Flask and HTML.

·         Now user will enter the website link in the front end.

 

 

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·         The extracted parameters of the user entered website link in the front end are given as input to our finalized algorithm to predict whether the user entered website is phishing website or not.

 

 

·         Finally the predicted output is displayed on the front end.