Detection of Obfuscated Attacks in Collaborative Recommender Systems

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“Detection of Obfuscated Attacks in Collaborative Recommender Systems” by Chad Williams, Bamshad Mobasher, Robin Burke, Jeff Sandvig, and Runa Bhaumik. In Proceedings of the ECAI'06 Workshop on Recommender Systems, (Held at the 17th European Conference on Artificial Intelligence (ECAI'06), Riva del Garda, Italy), Aug. 2006.

Abstract

The vulnerability of collaborative recommender systems has been well established; particularly to reverse-engineered attacks designed to bias the system in an attacker’s favor. Recent research has begun to examine detection schemes to recognize and defeat the effects of known attack models. In this paper we propose several techniques an attacker might use to modify an attack to avoid detection, and show that these obfuscated versions can be nearly as effective as the reverse-engineered models yet harder to detect. We explore empirically the impact of these obfuscated attacks against systems with and without detection, and discuss alternate approaches to reducing the effectiveness of such attacks.

Keywords: attack detection, bias profile injection, collaborative filtering, recommender systems, obfuscated attacks, attack models

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BibTeX entry:

@inproceedings{WMBSB06,
   author = {Chad Williams and Bamshad Mobasher and Robin Burke and Jeff
	Sandvig and Runa Bhaumik},
   title = {Detection of Obfuscated Attacks in Collaborative Recommender
	Systems},
   booktitle = {Proceedings of the ECAI'06 Workshop on Recommender Systems},
   address = {Held at the 17th European Conference on Artificial
	Intelligence (ECAI'06), Riva del Garda, Italy},
   month = aug,
   year = {2006},
   url =
	{http://proserver3-iwas.uni-klu.ac.at/ECAI06-Recommender-Workshop/workshop_proceedings.pdf}
}