Improving Sybil Detection via Graph Pruning and Regularization Techniques

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Huanhuan Zhang, Jie Zhang, Carol Fung, Chang Xu ;
Asian Conference on Machine Learning, PMLR 45:189-204, 2016.

Abstract

Due to their open and anonymous nature, online social networks are particularly vulnerable to Sybil attacks. In recent years, there has been a rising interest in leveraging social network topological structures to combat Sybil attacks. Unfortunately, due to their strong dependency on unrealistic assumptions, existing graph-based Sybil defense mechanisms suffer from high false detection rates. In this paper, we focus on enhancing those mechanisms by considering additional graph structural information underlying social networks. Our solutions are based on our novel understanding and interpretation of Sybil detection as the problem of partially labeled classification. Specifically, we first propose an effective graph pruning technique to enhance the robustness of existing Sybil defense mechanisms against target attacks, by utilizing the local structural similarity between neighboring nodes in a social network. Second, we design a domain-specific graph regularization method to further improve the performance of those mechanisms by exploiting the relational property of the social network. Experimental results on four popular online social network datasets demonstrate that our proposed techniques can significantly improve the detection accuracy over the original Sybil defense mechanisms.

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