Exploiting mobile social behaviors for Sybil detection

In this paper, we propose a Social-based Mobile Sybil Detection (SMSD) scheme to detect Sybil attackers from their abnormal contacts and pseudonym changing behaviors. Specifically, we first define four levels of Sybil attackers in mobile environments according to their attacking capabilities. We then exploit mobile users' contacts and their pseudonym changing behaviors to distinguish Sybil attackers from normal users. To alleviate the storage and computation burden of mobile users, the cloud server is introduced to store mobile user's contact information and to perform the Sybil detection. Furthermore, we utilize a ring structure associated with mobile user's contact signatures to resist the contact forgery by mobile users and cloud servers. In addition, investigating mobile user's contact distribution and social proximity, we propose a semi-supervised learning with Hidden Markov Model to detect the colluded mobile users. Security analysis demonstrates that the SMSD can resist the Sybil attackers from the defined four levels, and the extensive trace-driven simulation shows that the SMSD can detect these Sybil attackers with high accuracy.