Attack detection in recommender systems based on target item analysis
Wei Guo Zhou, Junhao Wen, Yun Sing Koh, Shafiq Alam, Gillian Dobbie · 2014
Recommender systems are highly vulnerable to attacks. Attackers who introduce biased ratings in order to affect recommendations, have been shown to be effective against collaborative filtering algorithms. In this paper, we study the use of statistical metrics to detect rating patterns of attackers. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analysing rating patterns between malicious profiles and genuine profiles in shilling attacks. Building upon this, we propose and evaluate an algorithm for detecting shilling attacks in recommender systems using a statistical approach. We look at two attack models: random attack and average attack. The experimental results show that our detection technique based on target item analysis is an effective approach in detecting shilling attacks for both the random and average attack model.