Unorganized malicious attacks detection

Abstract

Recommender system has attracted much attention during the past decade. However, collaborative filtering as a usual technique is vulnerable to malicious attacks that generate fake profiles to manipulate the system. Prior research has shown that attacks can significantly affect the robustness of the systems. Thus, many attack detection algorithms have been developed for better recommendation. Most previous approaches focus on organized malicious attacks, where the attack organizer fakes many user profiles using the same strategy to promote or demote an item. In this study, we analyze a different attack style: unorganized malicious attacks, where attackers fake a small number of user profiles to attack the same target item without any organizer. This attack style occurs in many real applications, which can significantly affect the robustness of a recommender system, yet relevant studies are inadequate. We conduct extensive experiments to study the performance of state-of-the-art attack detection approaches in unorganized malicious attack detection and discuss different approaches regarding their performance. Experimental results show that existing attack detection approaches cannot detect unorganized malicious attacks efficiently. By explaining the inefficiency of these attack detection approaches and the characteristics of unorganized malicious attacks in detail, we provide a possible research direction to develop new detection schemes for unorganized malicious attack detection.