SybilInfer is an algorithm for labelling nodes in a so- cial network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilis- tic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference approach to Sybil detection comes with the advantage label has an assigned probability, indi- cating its degree of certainty. We prove through analytical results as well as experiments on simulated and real-world network topologies that, given standard constraints on the adversary, SybilInfer is secure, in that it successfully dis- tinguishes between honest and dishonest nodes and is not susceptible to manipulation by the adversary. Further- more, our results show that SybilInfer outperforms state of the art algorithms, both in being more widely applica- ble, as well as providing vastly more accurate results.
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