SybilHunter: Hybrid graph-based sybil detection by aggregating user behaviors

Nowadays, online social networks (OSNs) support users in sharing their information and providing valuable data for various applications, e.g., rating and recommendation systems, search engine systems, etc. In such online social network-based applications, information quality is essential. However, the easy-to-use interactive user interfaces and free publication mechanisms facilitate malicious users to launch Sybil Attacks. They create fake identities, pollute user-generated content, and cause server information quality problems, such as buzz, rumor, spam, etc. Most existing graph-based sybil detection approaches only consider the static graph structure features and heavily rely on the prior knowledge of labeled nodes. “Limited-attack-edge” assumption, the critical assumption of these approaches has been proved invalid in many scenarios. In this paper, we propose SybilHunter, a hybrid graph-based sybil detection approach by aggregating user social behavior patterns. Our approach refines the OSN structure, quantifies nodes’ similarity according to the dynamic user behavior features to evaluate user pairs’ trustworthiness and consistency. Then it constructs a weighted-strong-social (WSS) graph, based on which SybilHunter outputs sybil nodes. We simulate and evaluate our approach under a Weibo dataset. The AUC of SybilHunter achieves 0.945, which is significantly higher than typical graph-based sybil detection methods, such as SybilRank, SybilWalk, etc.