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“Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation” by B. Mobasher, R. Burke, C. Williams, and R. Bhaumik. In Advances in Web Mining and Web Usage Analysis, vol. 4198 of Lecture Notes in Artificial Intelligence, (O. R. Zaïane O. Nasraoui and P. S. Yu, eds.), 2006, pp. 96-118.
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.
Keywords: web mining, data mining, recommender systems, attack detection, bias profile injection, collaborative filtering, pattern recognition, pattern mining
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BibTeX entry:
@incollection{MBWB06,
author = {B. Mobasher and R. Burke and C. Williams and R. Bhaumik},
editor = {O. Nasraoui, O. R. Za{\"\i}ane and P. S. Yu},
title = {Analysis and Detection of Segment-Focused Attacks Against
Collaborative Recommendation},
booktitle = {Advances in Web Mining and Web Usage Analysis},
series = {Lecture Notes in Artificial Intelligence},
volume = {4198},
pages = {96-118},
publisher = {Springer Berlin Heidelberg},
year = {2006},
url = {http://www.springerlink.com/content/d71t67748x47l408}
}