Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
SybilGuard: defending against sybil attacks via social networks
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Combatting financial fraud: a coevolutionary anomaly detection approach
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 2009 ACM symposium on Applied Computing
Temporal collaborative filtering with adaptive neighbourhoods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
DSybil: Optimal Sybil-Resistance for Recommendation Systems
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Rate it again: increasing recommendation accuracy by user re-rating
Proceedings of the third ACM conference on Recommender systems
Application of anomaly detection algorithms for detecting SYN flooding attacks
Computer Communications
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Recommender systems are vulnerable to attack: malicious users may deploy a set of sybils (pseudonymous, automated entities) to inject ratings in order to damage or modify the output of Collaborative Filtering (CF) algorithms. To protect against these attacks, previous work focuses on designing sybil profile classification algorithms, whose aim is to find and isolate sybils. These methods, however, assume that the full sybil profiles have already been input to the system. Deployed recommender systems, on the other hand, operate over time, and recommendations may be damaged while sybils are still injecting their profiles, rather than only after all malicious ratings have been input. Furthermore, system administrators do not know when their system is under attack, and thus when to run these classification techniques, thus risking to leave their recommender system vulnerable to attacks. In this work, we address the problem of temporal sybil attacks, and propose and evaluate methods for monitoring global, user and item behaviour over time, in order to detect rating anomalies that reflect an ongoing attack.