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Detecting Temporal Sybil Attacks

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Recommender systems are vulnerable to attack: malicious users may deploy a set of sybils to inject ratings in order to damage or modify the output of Collaborative Filtering (CF) algorithms. Previous work in the area focuses on designing sybil profile classification algorithms: to protect against attacks, the aim is to find and isolate any 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: recommendations may be damaged as sybils inject profiles (rather than only when all the malicious ratings have been input), and system administrators may not know when their system is under attack. 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. We conclude by discussing the consequences of our temporal defenses, and how attackers may design ramp-up attacks in order to circumvent them.

Bio: Neal is a Research Fellow in the Department of Computer Science, University College London, working on the EU iTour project with Dr Capra (http://www.itourproject.com). His PhD thesis (to be imminently submitted) was supervised by Prof. Hailes and titled “Evaluating Collaborative Filtering Over Time;” the thesis dealt with modeling, evaluating, and improving the temporal performance of recommender systems. More details are available on: http://www.cs.ucl.ac.uk/staff/n.lathia

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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