AGSA: Anti-similarity Group Shilling Attacks

Peng Wang, Lingtao Qi, Haiping Huang, Feng Li, Congxiang Yu

Abstract: With the rapid development of e-commerce, the security issues of recommender systems have been widely investigated. Malicious users can benefit from injecting great quantities of fake profiles into recommender systems to reduce the frequency of undesired recommendation items. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Although a multitude of studies have been devoted to shilling attack modeling and detection, few of them focus on group shilling attack. The attackers in a shilling group work together to manipulate the output of the recommender system. Based on the model of the loose version of Group Shilling Attack Generation Algorithm (GSAGenl), we design an anti-similarity group shilling attack model (AGSA). AGSA rationalizes the evaluation time interval of the group attack and strengthens the destructive powers of the group shilling attacks.