Phishing Website Detection using Machine Learning

Abstract

Phishing attacks, a prevalent and significant form of cybercrime, involve attackers masquerading as reputable entities to deceive individuals into revealing sensitive details such as usernames, passwords, and credit card information. Deceptive websites are commonly used in these attacks, appearing legitimate and underscoring the need for individuals and organizations to heighten their awareness and implement stronger and more advanced detection techniques. By luring sensitive information through deceptive websites, phishing attacks represent a serious cybersecurity threat. In this research, the effectiveness of machine learning algorithms, specifically the Gradient Boosting Classifier, in identifying phishing websites to enhance accuracy and response time is being assessed.