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KDD
2005
ACM

Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation

14 years 4 months ago
Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers can easily introduce biased data in an attempt to force the system to "adapt" in a manner advantageous to them. Our research in secure personalization is examining a range of attack models, from the simple to the complex, and a variety of recommendation techniques. In this chapter, we explore an attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against both user-based and item-based collaborative filtering. We also introduce a detection model that can significantly decrease the impact of this attack.
Bamshad Mobasher, Robin D. Burke, Chad Williams, R
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2005
Where KDD
Authors Bamshad Mobasher, Robin D. Burke, Chad Williams, Runa Bhaumik
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