Detection of Obfuscated Attacks in Collaborative Recommender Systems

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.