Amalgamate Phishing Attack Detection using Machine Learning

Abstract

Malicious Web sites largely promote the growth of Internet criminal activities and constrain the development of Web services. As a result, there has been strong motivation to develop systemic solution to stopping the user from visiting such Web sites. We propose a learning based approach to classifying Web sites into 3 classes: Benign, Spam and Malicious. Our mechanism only analyzes the Uniform Resource Locator (URL) itself without accessing the content of Web sites. Thus, it eliminates the run-time latency and the possibility of exposing users to the browser based vulnerabilities. By employing learning algorithms, our scheme achieves better performance on generality and coverage compared with blacklisting service.