Performance Analysis of Classifying URL Phishing Using Recursive Feature Elimination

Phishing is a popular cyber-attack that tricks users into revealing confidential data such as login passwords or account details, on a website that appears official. Many methods for identifying phishing attacks, particularly the use of machine learning, have been presented in recent years. This study examines the impact of using the Recursive Feature Elimination technique on the classification accuracy of phishing websites by analyzing several Machine Learning algorithms for identifying phishing websites based on URL attributes. According to the experimental findings, RFE (Recursive Feature Elimination) was essential in reducing the effect of irrelevant features on the accuracy of various classifiers. We also assessed the key features included in both datasets, which are crucial for classifying URL-based phishing websites. Our research uncovered four essential features: Page Rank, Links in Tags, Statical_Report and Having_Sub_Domain.