Sybil Detection in Online Social Networks (OSNs)

Peer to peer and distributed systems are generally susceptible to sybil attacks. Online social networks (OSNs), due to their fat user base and open access nature are also prone to such attacks. Current state-of-art algorithms for sybil attack detection make use of the inherent social graph created among registered users of OSN service. They rely on the inherent trust relationship among these users. No effort is made to combine other characteristic behavior of sybil users with properties of social graph of OSNs to improve detection accuracy of sybil attacks. Sybil identities are also used as gateways for spreading spam content in OSNs. The proposed approach exploits this behavior of sybil users to improve detection accuracy of existing sybil detection algorithms. In the proposed approach, content generated/published by each user is used along with the topological properties of the social graph of registered users. A machine learning model is used for assigning a fractional value called "trust value" which denotes the amount of legitimate content generated by the user. A modification to sybil detection algorithm is proposed which makes use of the trust value of each user to improve the accuracy of detecting a sybil identity. Real dataset from Facebook is crawled and used for analysis and experiments. Analytical results show the superiority of proposed solution. Results are compared with SybilGuard and SybilShield which shows ~14% decrease in false positive rates with very minimal effect on acceptance rate or false negative rate of the sybil detection algorithms. Also, the proposed modification does not affect the performance of existing sybil detection algorithms and can be implemented in a distributed manner.