LAUSR

User-Review Social Networks (URSNs) now become the targets of Sybil attacks, where fake reviews are posted by attackers to raise the reputation of listed services or products. Unlike previous fake accounts on Twitter or Weibo, Sybil attackers on URSNs are also genuine users in many cases, which presents a challenge for the existing fake review detection system. Hence, the main purpose of detecting Sybil attackers is to profile abnormal behaviors of users, and we propose a novel Sybil detection model named MUSH to extract long-term features of user behaviors on URSNs. First, aiming to measure the uncertainty of user behaviors, four entropy-based preference models are designed to quantify user preferences, including catering preferences, price preferences, word-of-mouth preferences, and rating preferences. Second, in order to extract temporal logic features, a novel multi-stimuli Hawkes process is introduced by combining external incentives and internal incentives, which can detect abnormal event sequences of posted reviews. This approach is quite different from previous solutions which mostly use direct/indirect graph models or user-related features for detection. Finally, by integrating entropy-based preferences with temporal logic features, a smart Sybil detection model is proposed based on a binary classification approach. Extensive experimental results indicate that MUSH can effectively detect Sybil attackers with a high detection accuracy. By comparing with other approaches, MUSH is quite suitable for adversarial network environments, which could provide better security services to social networks.