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Sybil detection in social networks is a basic security research problem.Structure-based methods have been shown to be promising at detecting Sybils.Existing structure-based methods can be classified into Random Walk (RW)-basedmethods and Loop Belief Propagation (LBP)-based methods. RW-based methodscannot leverage labeled Sybils and labeled benign users simultaneously, whichlimits their detection accuracy, and/or they are not robust to noisy labels.LBP-based methods are not scalable and cannot guarantee convergence. In thiswork, we propose SybilSCAR, a novel structure-based method to detect Sybils insocial networks. SybilSCAR is Scalable, Convergent, Accurate, and Robust tolabel noise. We first propose a framework to unify RW-based and LBP-basedmethods. Under our framework, these methods can be viewed as iterativelyapplying a (different) local rule to every user, which propagates labelinformation among a social graph. Second, we design a new local rule, whichSybilSCAR iteratively applies to every user to detect Sybils. We compareSybilSCAR with state-of-the-art RW-based and LBP-based methods theoreticallyand empirically. Theoretically, we show that, with proper parameter settings,SybilSCAR has a tighter asymptotical bound on the number of Sybils that arefalsely accepted into a social network than existing structure-based methods.Empirically, we perform evaluation using both social networks with synthesizedSybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with realSybils. Our results show that 1) SybilSCAR is substantially more accurate andmore robust to label noise than state-of-the-art RW-based methods; 2) SybilSCARis more accurate and one order of magnitude more scalable than state-of-the-artLBP-based methods.

Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation