Detection of collusion behaviors in online reputation systems

Online reputation systems are gaining popularity. Dealing with collaborative unfair ratings in such systems has been recognized as an important but difficult problem. The current defense mechanisms focus on analyzing rating values for individual products. In this paper, we propose a scheme that detects collaborative unfair raters based on similarity in their rating behaviors. The proposed scheme integrates abnormal detection in both rating-value domain and the user-domain. To evaluate the proposed scheme in realistic scenarios, we design and launch a cyber competition, in which attack data from real human users are collected. The proposed system is evaluated through experiments using real attack data. The proposed scheme can accurately detect collusion behaviors and therefore significantly reduce the damage caused by collaborative dishonest users.