Phishing Website Detection using Machine Learning

Abstract

Phishing is a common attack on credulous people by making them to disclose their unique information using counterfeit websites. The objective of phishing website URLs is to purloin the personal information like user name, passwords and online banking transactions. Phishers use the websites which are visually and semantically similar to those real websites. As technology continues to grow, phishing techniques started to progress rapidly and this needs to be prevented by using anti-phishing mechanisms to detect phishing. Machine learning is a powerful tool used to strive against phishing attacks. In this article, we proposed 5 different algorithms in machine learning to analyse the URLs. The accuracy of the Existing method is approximately 94%, and we have implemented it as 95.235% in the Proposed method. Here we used 5 classifiers which are Random Forest Classifier, AdaBoost Classifier, XGBoost Classifier, Support Vector Machine, Gradient Boosting Classifier. Among all these Classifiers, Random Forest Classifier gives the highest accuracy.