Biblio

Mobile Social Networks have become one of the most convenient services for users to share information everywhere. This crowdsourced information is often meaningful and recommended to users, e.g., reviews on Yelp or high marks on Dianping, which poses the threat of Sybil attacks. To address the problem of Sybil attacks, previous solutions mostly use indirect/direct graph model or clickstream model to detect fake accounts. However, they are either dependent on strong connections or solely preserved by servers of social networks. In this paper, we propose a novel predictable approach by exploiting users' custom patterns to distinguish Sybil attackers from normal users for the application of recommendation in mobile social networks. First, we introduce the entropy of spatial-temporal features to profile the mobility traces of normal users, which is quite different from Sybil attackers. Second, we develop discriminative entropy-based features, i.e., users' preference features, to measure the uncertainty of users' behaviors. Third, we design a smart Sybil detection model based on a binary classification approach by combining our entropy-based features with traditional behavior-based features. Finally, we examine our model and carry out extensive experiments on a real-world dataset from Dianping. Our results have demonstrated that the model can significantly improve the detection accuracy of Sybil attacks.