Defending Grey Attacks by Exploiting Wavelet Analysis in Collaborative Filtering Recommender Systems

TL;DR

This paper introduces a wavelet analysis-based detection method to identify grey shilling attacks in collaborative filtering recommender systems, focusing on hidden rating behaviors that traditional methods overlook.

Contribution

It proposes a novel approach combining rating deviation, novelty, and popularity features with wavelet transform to detect subtle grey attacks in recommender systems.

Findings

Effective detection of grey attacks demonstrated on Book-Crossing and HetRec-2011 datasets.

Outperforms benchmarked methods in accuracy and robustness.

Validates the approach's ability to identify hidden malicious rating behaviors.

Abstract

"Shilling" attacks or "profile injection" attacks have always major challenges in collaborative filtering recommender systems (CFRSs). Many efforts have been devoted to improve collaborative filtering techniques which can eliminate the "shilling" attacks. However, most of them focused on detecting push attack or nuke attack which is rated with the highest score or lowest score on the target items. Few pay attention to grey attack when a target item is rated with a lower or higher score than the average score, which shows a more hidden rating behavior than push or nuke attack. In this paper, we present a novel detection method to make recommender systems resistant to such attacks. To characterize grey ratings, we exploit rating deviation of item to discriminate between grey attack profiles and genuine profiles. In addition, we also employ novelty and popularity of item to construct rating series. Since it is difficult to discriminate between the rating series of attacker and genuine users, we incorporate into discrete wavelet transform (DWT) to amplify these differences based on the rating series of rating deviation, novelty and popularity, respectively. Finally, we respectively extract features from rating series of rating deviation-based, novelty-based and popularity-based by using amplitude domain analysis method and combine all clustered results as our detection results. We conduct a list of experiments on both the Book-Crossing and HetRec-2011 datasets in diverse attack models. Experimental results were included to validate the effectiveness of our approach in comparison with the benchmarked methods.