LAUSR

Collaborative Filtering, though a successful recommendation technique is vulnerable to shilling attacks due to its open nature. These attacks alter recommendations being generated for the user by inserting fake user profiles in the database. To minimize the bias introduced in the recommendation process, many machine learning methods have been explored and shown excellent results. However, supervised machine learning detection techniques are restricted to hand-designed features while unsupervised detection techniques require prior knowledge about fake profiles. In this paper, we propose a novel approach namely, ShillDetector for the detection of shilling attacks based on the recently proposed swarm intelligence technique, grey wolf optimization. The proposed approach works as a dimensionality reduction technique taking advantage of high correlation among shillers and removing correlated features that are redundant. Further, it works directly on the rating matrix, does not require hand-designed features, prior knowledge of attack profiles, or any training time. The performance of ShillDetector has been evaluated on the MovieLens dataset consisting of 100 K ratings. Experimental results depict that ShillDetector outperformed two state-of-the-art approaches, namely, SVM-TIA and PCA-VarSelect approaches with an average precision of 0.99 in case of average attack taken over different attack sizes, viz, 1%, 2%, 5%, and 10%.