PSD: Practical Sybil detection schemes using stickiness and persistence in online recommender systems

Abstract

The main function of recommender systems (RSs) is to recommend user-customized information to customers or system users. Correct and useful information is crucial for both customers and service providers. The influence of RSs is expanding over the Internet. However, criminal users try to manipulate the results of RSs with fake identities (i.e., Sybils) for financial gain. Effective metrics are consequently required for defense against Sybil attack. In this paper, we first explore two metrics, stickiness and persistence, from the perspective of the RS security domain. We then propose practical detecting schemes, Dynamic Sybil Attack Monitoring on Recommender Systems (DySy-Rec) and Fuzzy rule-based DySy-Rec (FDySy-Rec), which apply stickiness and persistence in two real datasets from real movie RSs. To demonstrate the effectiveness and potential of DySy-Rec and FDySy-Rec, we conducted extensive experiments on the inclusion of more diverse and smart types of attacks. The experimental results show that the proposed schemes achieve substantial performance improvement compared with previous statistical approaches in terms of precision and recall. Finally, the results confirm the practical possibilities of exploiting stickiness and persistence in the fight against dynamic Sybil attacks in online RSs.