Segment-Based Injection Attacks against Collaborative Filtering Recommender Systems

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“Segment-Based Injection Attacks against Collaborative Filtering Recommender Systems” by R. Burke, B. Mobasher, R. Bhaumik, and C. Williams. In Proceedings of the 2005 International Conference on Data Mining (ICDM'05), (Houston, Texas), Nov. 2005.

Abstract

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 item-based 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.

Keywords: profile injection attacks, collaborative filtering, recommender systems, segmented attacks

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BibTeX entry:

@inproceedings{BMBW05,
   author = {R. Burke and B. Mobasher and R. Bhaumik and C. Williams},
   title = {Segment-Based Injection Attacks against Collaborative
	Filtering Recommender Systems},
   booktitle = {Proceedings of the 2005 International Conference on Data
	Mining (ICDM'05)},
   address = {Houston, Texas},
   month = nov,
   year = {2005},
   url = {http://dx.doi.org/10.1109/ICDM.2005.127}
}