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.

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Robin D. Burke, Bamshad Mobasher, Runa Bhaumik, Chad Williams
• Keywords: Collaborative filtering Recommender, Data Mining, ICDM 2005, Significant Vulnerabilities, System’s Recommendations