A novel shilling attack detection method

Abstract

Recommender systems provide an impressive way to overcome information overload problem. However, they are vulnerable to profile injection or shilling attacks. Malicious users and/or parties might construct fake profiles and inject them into user-item databases to increase or decrease the popularity of some target products. Hence, they may have an effective impact on produced predictions. To eliminate such malicious impact, detecting shilling profiles becomes imperative. In this work, we propose a novel shilling attack detection method for particularly specific attacks based on bisecting k-means clustering approach, which provides that attack profiles are gathered in a leaf node of a binary decision tree. After evaluating our method, we perform experiments using a benchmark data set to analyze it with respect to success of attack detection. Our empirical outcomes show that the method is extremely successful on detecting specific attack profiles like bandwagon, segment, and average attack.