Shill Block

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.