Enhancing Sybil Detection via Social-Activity Networks: A Random Walk Approach

Abstract

Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users activities to assist sybil detection. We first develop the social and activity network (SAN), a two-layer hyper-graph that unifies users friendships and their activities, to fully utilize users activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil_SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil_SAN, and derive the number of rounds needed to guarantee the convergence. Extensive experiments on both synthetic and real-world datasets show that Sybil SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy.