Web Phishing detection based on graph mining

Web Phishing (Phishing) uses social engineering technique through short messages, emails and IMs to induce users to visit faked website to get sensitive information. With detecting method for phishing continually proposed and applied, the threat of web phishing has already reduced at a great extent. However, since each type of detection has limitation, phishing attackers can modify their strategies at a relatively low cost to avoid detection accordingly. Facing the defects of current detection, we mainly focus on the behavior pattern of phishing websites. We analyze real IP flows from ISP and propose a detecting method based on Graph Mining with Belief Propagation. The experiment suggested that our algorithm has decent accuracy and runtime efficiency. As we have considered distributed computation while designing the algorithm, it will be easy to replicate our model in popular distributed processing frameworks.