Abstract
Collaborative recommender systems have been shown to be vulnerable to profile injection attacks. By injecting a large number of biased profiles into a system, attackers can manipulate the predictions of targeted items. To decrease this risk, researchers have begun to study mechanisms for detecting and preventing profile injection attacks. In prior work, we proposed several attributes for attack detection and have shown that a classifier built with them can be highly successful at identifying attack profiles. In this paper, we extend our work through a more detailed analysis of the information gain associated with these attributes across the dimensions of attack type and profile size. We then evaluate their combined effectiveness at improving the robustness of user based recommender systems.
This research was supported in part by the National Science Foundation Cyber Trust program under Grant IIS-0430303.
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Williams, C.A., Mobasher, B., Burke, R., Bhaumik, R. (2007). Detecting Profile Injection Attacks in Collaborative Filtering: A Classification-Based Approach. In: Nasraoui, O., Spiliopoulou, M., Srivastava, J., Mobasher, B., Masand, B. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2006. Lecture Notes in Computer Science(), vol 4811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77485-3_10
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DOI: https://doi.org/10.1007/978-3-540-77485-3_10
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