Shilling Attack Detection in Recommender Systems via Selecting Patterns Analysis

Abstract

SUMMARYCollaborative filtering (CF) has been widely used in recommender systems to generate personalized recommendations. However, recommender systems using CF are vulnerable to shilling attacks, in which attackers inject fake profiles to manipulate recommendation results. Thus, shilling attacks pose a threat to the credibility of recommender systems. Previous studies mainly derive features from characteristics of item ratings in user profiles to detect attackers, but the methods suffer from low accuracy when attackers adopt new rating patterns. To overcome this drawback, we derive features from properties of item popularity in user profiles, which are determined by users' different selecting patterns. This feature extraction method is based on the prior knowledge that attackers select items to rate with man-made rules while normal users do this according to their inner preferences. Then, machine learning classification approaches are exploited to make use of these features to detect and remove attackers. Experiment results on the MovieLens dataset and Amazon review dataset show that our proposed method improves detection performance. In addition, the results justify the practical value of features derived from selecting patterns.

Multiview Ensemble Method for Detecting Shilling Attacks in Collaborative Recommender Systems

“…As shown in Figure 2, most ratings of genuine profiles are concentrated on the left side of horizontal axis. This indicates that the genuine users are likely to rate the popular items, which is consistent with the observation in [28]. By contrast, the ratings of shilling profiles are uniformly distributed along the horizontal axis.…”

“…And then the user features were used by SVM for detection. Li et al [27,28] extracted the user features according to the item popularity distributions. Unfortunately, in these methods, the item popularity is simply computed and observed as a static value, which can be easily influenced by noise and manipulated by attacks.…”

“…In practice, the shilling profiles are usually far fewer than genuine ones, and the detection method based on supervised classification can be regarded as the imbalanced classification problem [11]. A large number of experiment results [2][3][4][5][6][7][8][9][10][26][27][28] showed that the single classifier became worse in low filler sizes and attack sizes, so ensemble methods were used to alleviate such problems. Zhang et al [18,25] used ensemble methods to detect the attacks based on the bagging method.…”

“…In this dataset, 5055 reviewers are labelled. Moreover, the information of 8915 reviewer groups is provided [28,44].…”

“…To defend collaborative recommender systems, a number of methods have been put forward to detect the shilling profiles and most of them mainly rely on one kind of usergenerated information (i.e., single view) such as rating values [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], rating time [19][20][21][22][23], and item popularity [24][25][26][27][28]. Due to the diversity and variability of attackers' strategies, it is very difficult to fully characterize the shilling profiles from single view information [29], and many conventional detection methods suffer from poor precision when detecting different types of attacks [30].…”