Unsupervised detection of obfuscated diverse attacks in recommender systems

Biased ratings of attack profiles have a significant impact on the effectiveness of collaborative recommender systems. Previous work has shown standard memory-based recommendation algorithms, such as k-nearest neighbor (kNN), susceptible to the attacks compared with model-based collaborative filtering (CF) algorithms. An obfuscated diverse attack strategy made model-based algorithms vulnerable to attacks. Attack profiles generated with this strategy are also able to avoid principal component analysis (PCA)-based detection. This paper proposes an algorithm to detect obfuscated diverse attack profiles. Profiles' pairwise covariance with each other is used to separate attack profiles from genuine profiles. Through extensive experiments, we demonstrate that our algorithm detects these attack profiles with high accuracy.