Detection of Obfuscated Attacks in Collaborative Recommender Systems

This paper discusses the various types of obfuscated attacks and their characteristics. The vulnerability of collaborative recommender systemshas 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. This paper proposes some ways of detecting an avoiding these attacks. We explore empirically the impact of these obfuscated attacks against systems with and without detection, and discuss alternate approaches to reducingthe effectiveness of such attacks.

link: http://maya.cs.depaul.edu/~mobasher/papers/wmbsb-ecai-ws06.pdf

Lies and Propaganda: Detecting Spam Users in Collaborative Filtering

This paper talks about the different ways of detecting spam users. It discusses and explains various algorithms for the same. Lies and Propaganda may be spread bya malicious user who may have an interest in promoting,or downplaying the popularity of an item. By doing thissystematically, with eithermultiple identities, or by involving more people, a few malicious user votes and profiles can be injected into a collaborative recommender system. In this work, provide a simple unsupervised algorithm is provided, which exploitsstatistical properties of effective spam profiles to provide a highly accurate and fast algorithm for detecting spam.

link to paper: http://delivery.acm.org/10.1145/1220000/1216307/p14-mehta.pdf?key1=1216307&key2=7374527021&coll=GUIDE&dl=GUIDE&CFID=62245235&CFTOKEN=96995055