Defending multiple-user-multiple-target attacks in online reputation systems
Date of Original Version
As online reputation systems are playing increasingly important roles in reducing risks of online interactions, attacks against such systems have evolved rapidly. Nowadays, some powerful attacks are conducted by companies that make profit through manipulating reputation of online items for their customers. These items can be products (e.g. in Amazon), businesses (e.g. hotels in travel sites), and digital content (e.g. videos in Youtube). In such attacks, colluded malicious users play wellplanned strategies to manipulate reputation of multiple target items. To address these attacks, we propose a defense scheme that (1) sets up heterogeneous thresholds for detecting suspicious items and (2) identifies target items based on correlation analysis among suspicious items. The proposed scheme and two other comparison schemes are evaluated by a combination of real user data and simulation data. The proposed scheme demonstrates significant advantages in detecting malicious users, recovering reputation scores of target items, and reducing interference to normal items. © 2011 IEEE.
Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
Liu, Yuhong, Yan Sun, and Ting Yu. "Defending multiple-user-multiple-target attacks in online reputation systems." Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011 , (2011): 425-434. doi:10.1109/PASSAT/SocialCom.2011.227.