Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation Export

@incollection{citeulike:5292920, abstract = {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.}, author = {Mobasher, Bamshad and Burke, Robin and Williams, Chad and Bhaumik, Runa}, booktitle = {WEBKDD}, citeulike-article-id = {5292920}, citeulike-linkout-0 = {http://dblp.uni-trier.de/rec/bibtex/conf/kdd/MobasherBWB05}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/11891321_6}, citeulike-linkout-2 = {http://www.springerlink.com/content/d71t67748x47l408}, doi = {10.1007/11891321_6}, isbn = {3-540-46346-1}, journal = {Advances in Web Mining and Web Usage Analysis}, keywords = {attack, collaborative\_filtering, collaborative\_recommendation, detection, model, personalization, profiles, segment, tastes, vulnerabilities}, pages = {96--118}, posted-at = {2009-10-04 16:05:15}, priority = {0}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation}, url = {http://dx.doi.org/10.1007/11891321_6}, volume = {4198}, year = {2006} }

TY - CHAP ID - citeulike:5292920 L3 - citeulike-article-id:5292920 N2 - 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. JF - Advances in Web Mining and Web Usage Analysis SE - Lecture Notes in Computer Science SN - 3-540-46346-1 EP - 118 TI - Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation PB - Springer VL - 4198 T2 - WEBKDD SP - 96 KW - attack KW - collaborative_filtering KW - collaborative_recommendation KW - detection KW - model KW - personalization KW - profiles KW - segment KW - tastes KW - vulnerabilities AU - Mobasher, Bamshad AU - Burke, Robin AU - Williams, Chad AU - Bhaumik, Runa PY - 2006/// UR - http://dx.doi.org/10.1007/11891321_6 ER -