GROUP SHILLING ATTACKS DETECTION USING BISECTING K-MEANS

Abstract

Existing shilling attack detection approaches focus mainly on identifying individual attackers in online
recommender systems and rarely address the detection of group shilling attacks in which a group of
attackers colludes to bias the output of an online recommender system by injecting fake profiles. In this
article, we propose a group shilling attack detection method based on the bisecting K-means clustering
algorithm. First, we extract the rating track of each item and divide the rating tracks to generate candidate
groups according to a fixed time interval. Second, we propose item attention degree and user activity to
calculate the suspicious degrees of candidate groups. Finally, we employ the bisecting K-means algorithm
to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. The
results of experiments on the Netflix and Amazon data sets indicate that the proposed method
outperforms the baseline methods