Using Domain Top-page Similarity Feature in Machine Learning-Based Web Phishing Detection

This paper presents a study on using a concept feature to detect web phishing problem. Following the features introduced in Carnegie Mellon Anti-phishing and Network Analysis Tool (CANTINA), we applied additional domain top-page similarity feature to a machine learning based phishing detection system. We preliminarily experimented with a small set of 200 web data, consisting of 100 phishing webs and another 100 non-phishing webs. The evaluation result in terms of f-measure was up to 0.9250, with 7.50% of error rate.