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Segment-Based Injection Attacks against Collaborative Filtering Recommender Systems

9 years 5 months ago
Segment-Based Injection Attacks against Collaborative Filtering Recommender Systems
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Researchers have shown that attackers can manipulate a system’s recommendations by injecting biased profiles into it. In this paper, we examine attacks that concentrate on a targeted set of users with similar tastes, biasing the system’s responses to these users. We show that such attacks are both pragmatically reasonable and also highly effective against both user-based and itembased algorithms. As a result, an attacker can mount such a “segmented” attack with little knowledge of the specific system being targeted and with strong likelihood of success.
Robin D. Burke, Bamshad Mobasher, Runa Bhaumik, Ch
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICDM
Authors Robin D. Burke, Bamshad Mobasher, Runa Bhaumik, Chad Williams
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