SybilFrame: A Defense-in-Depth Framework for Structure-Based Sybil Detection

TL;DR

SybilFrame is a comprehensive defense framework that effectively detects Sybil attacks in social networks by integrating prior information, outperforming previous methods on large-scale real-world datasets.

Contribution

It introduces SybilFrame, a novel defense-in-depth framework that relaxes oversimplified assumptions and incorporates prior information for improved Sybil detection.

Findings

Outperforms previous structure-based approaches by an order of magnitude.

Effective on large-scale datasets, including Twitter with 20M nodes.

Validated on both synthetic and real-world social network topologies.

Abstract

Sybil attacks are becoming increasingly widespread, and pose a significant threat to online social systems; a single adversary can inject multiple colluding identities in the system to compromise security and privacy. Recent works have leveraged the use of social network-based trust relationships to defend against Sybil attacks. However, existing defenses are based on oversimplified assumptions, which do not hold in real world social graphs. In this work, we propose SybilFrame, a defense-in-depth framework for mitigating the problem of Sybil attacks when the oversimplified assumptions are relaxed. Our framework is able to incorporate prior information about users and edges in the social graph. We validate our framework on synthetic and real world network topologies, including a large-scale Twitter dataset with 20M nodes and 265M edges, and demonstrate that our scheme performs an order of magnitude better than previous structure-based approaches.