Graph embedding-based approach for detecting group shilling attacks in collaborative recommender systems

Abstract

Over the past decade, many approaches have been presented to detect shilling attacks in collaborative recommender systems. However, these approaches focus mainly on detecting individual attackers and rarely consider the collusive shilling behaviors among attackers, i.e., a group of attackers working together to bias the output of collaborative recommender systems by injecting fake profiles. Such shilling behaviors are generally termed group shilling attacks, which are more harmful to collaborative recommender systems than traditional shilling attacks. In this paper, we propose a graph embedding-based method to detect group shilling attacks in collaborative recommender systems. First, we construct a user relationship graph by analyzing the user rating behaviors and use a graph embedding method to obtain the low-dimensional vector representation of each node in the user relationship graph. Second, we employ the k-means++ clustering algorithm to obtain candidate groups based on the generated user feature vectors. Finally, we calculate the suspicious degree of each candidate group according to the attack group detection indicators and use the Ward’s hierarchical clustering method to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. The experimental results on the Amazon and Netflix datasets show that the proposed method outperforms the baseline methods in detection performance.