Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation

• Published in: Advances in Web Mining and Web Usage Analysis pp. 96 - 118
• Main Authors: Mobasher, Bamshad, Burke, Robin, Williams, Chad, Bhaumik, Runa
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006
• Series: Lecture Notes in Computer Science
• Subjects: Attack Model; Average Attack; Recommendation Algorithm; Recommender System; Target Item
• ISBN: 3540463461; 9783540463467
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11891321_6

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.