Poster: User-Centric Phishing Threat Detection

This paper presents a context-aware phishing threat detection model from users’ behavioral perspectives. The context of users’ information accesses is investigated to explore the users’ browsing behaviors that confront phishing situations. Large-scale experiments show that our approach achieves an accuracy of 0.9973 and an F1 score of 0.9311 for predicting the phishing threats of users’ next accesses without intelligent content analysis. Error analysis indicates that our proposed model results in a favorably low false positive rate of 0.0006. In practice, our proposed model is complementary to the existing anti-phishing techniques for cost-effectively blocking phishing threats with wisdom of the crowds. Keywords—collaborative filtering; browsing behaviors; collective intelligence; context-aware category prediction