SybilFrame: A Defense-in-Depth Framework for Structure-Based Sybil Detection
This work addresses the threat of Sybil attacks for online social systems, offering a more robust defense that is incremental over prior methods by relaxing assumptions and incorporating additional information.
The authors tackled the problem of Sybil attacks in online social systems by proposing SybilFrame, a defense-in-depth framework that relaxes oversimplified assumptions in existing methods and incorporates prior information about users and edges. They demonstrated that SybilFrame performs an order of magnitude better than previous structure-based approaches on synthetic and real-world datasets, including a large-scale Twitter dataset with 20 million nodes and 265 million edges.
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.