Detecting shilling attacks in social recommender systems based on time series analysis and trust features

In social recommender systems or trust-based recommender systems, malicious users can bias the recommendations by injecting a large number of fake profiles and by building bogus trust relationships. The existing shilling attack detection methods suffer from low precision when detecting attacks in social recommender systems because they focus mainly on the rating pattern differences between attack profiles and genuine ones and ignore the trust relationships between users. In this paper, we propose an approach for detecting shilling attacks in social recommender systems based on time series analysis and trust features (TSA–TF). Firstly, we construct rating distribution time series for items and propose a dynamic rating distribution prediction model to detect suspicious items by using a single exponential smoothing method. Then, we filter out a part of genuine user profiles by analyzing suspicious items and obtain the set of suspicious user profiles. Secondly, we propose four features by combining rating patterns and trust relationships and train a support vector machine (SVM) classifier to discriminate attack profiles in the set of suspicious user profiles. Experiments on the CiaoDVD dataset and Epinions dataset show that the proposed approach can improve the detection precision while maintaining a high recall.