Abstract
To protect recommender systems against shilling attacks, a variety of detection methods have been proposed over the past decade. However, these methods focus mainly on individual features and rarely consider the lockstep behaviours among attack users, which suffer from low precision in detecting group shilling attacks. In this work, we propose a three-stage detection method based on strong lockstep behaviours among group members and group behaviour features for detecting group shilling attacks. First, we construct a weighted user relationship graph by combining direct and indirect collusive degrees between users. Second, we find all dense subgraphs in the user relationship graph to generate a set of suspicious groups by introducing a topological potential method. Finally, we use a clustering method to detect shilling groups by extracting group behaviour features. Extensive experiments on the Netflix and sampled Amazon review datasets show that the proposed approach is effective for detecting group shilling attacks in recommender systems, and the F1-measure on two datasets can reach over 99 percent and 76 percent, respectively.
Highlights
Information resources are growing explosively with the rapid development of Internet, which causes information overload
As attack users in the group work together and strategically generate attack profiles, the detection methods proposed for detecting traditional attacks become ineffective for group shilling attacks. erefore, how to improve the detection performance for group shilling attacks has become a key issue in the recommender systems
To address the above limitations, we propose an unsupervised method for detecting group shilling attacks in recommender systems based on topological potential theory and group behaviour features, which is named as TP-GBF
Summary
Information resources are growing explosively with the rapid development of Internet, which causes information overload. Traditional attack models have been well studied, such as random attack, average attack, bandwagon attack, AoP attack, Love/hate attack, etc In these attacks, attack users try to promote or demote the same target item but select filler items separately. E behaviour wherein a group of attack users collude to promote or demote the recommendation of a set of target items has been termed group shilling attacks [6, 7]. [6], Su et al first proposed the concept of group shilling attacks in recommender systems and mentioned two attack scenarios. In these scenarios, multiple attackers are well organized to conceal their intentions. Many shilling groups may coexist in a recommender system
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