Shilling attack detection in binary data: a classification approach

Reliability of a recommender system is extremely substantial for the continuity of the system. Malicious users may harm the reliability of predictions by injecting fake profiles called shilling attacks into the system. Therefore, the detection of such attacks is vital for a recommender system. Thus, many shilling attack detection methods have been studied. However, the proposed solutions work only on numerical rating based recommender systems. On the other hand, it has been shown that collaborative filtering systems utilizing binary ratings are also vulnerable to shilling attacks. In this work, we propose a detection method, which finds out six well-known shilling attack models against binary ratings-based collaborative filtering systems. Besides deriving generic attributes from user profiles, we generate additional model-specific attributes in order to deal with fake profiles. Our empirical results show that the proposed method successfully detects attack profiles even with low attack size and filler size values.