Exploiting Trust and Distrust Information to Combat Sybil Attack in Online Social Networks

Abstract

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 leveraging 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.