Due to open and anonymous nature, online social networks are particularly vulnerable to the Sybil attack, in which a malicious user can fabricate many dummy identities to attack the systems. Recently, there is a flurry of interests to leverage social network structure for Sybil defense. However, most of graph-based approaches pay little attention to the distrust information, which is an important factor for uncovering more Sybils. In this paper, we propose an unified ranking mechanism by

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... ing trust and distrust in social networks against such kind of attacks based on a variant of the PageRank-like model. Specifically, we first use existing topological anti-Sybil algorithms as a subroutine to produce reliable Sybil seeds. To enhance the robustness of these approaches against target attacks, we then also introduce an effective similarity-based graph pruning technique utilizing local structure similarity. Experiments show that our approach outperforms existing competitive methods for Sybil detection in social networks.