SYBILFUSE: Combining local attributes with global structure to perform robust sybil detection

Peng Gao, Binghui Wang, Neil Zhenqiang Gong, Sanjeev R. Kulkarni, Kurt Thomas, Prateek Mittal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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 social network-based trust relationships to defend against Sybil attacks. However, existing defenses are based on oversimplified assumptions about network structure, which do not necessarily hold in real-world social networks. Recognizing these limitations, we propose SYBILFUSE, a defense-in-depth framework for Sybil detection when the oversimplified assumptions are relaxed. SYBILFUSE adopts a collective classification approach by first training local classifiers to compute local trust scores for nodes and edges, and then propagating the local scores through the global network structure via weighted random walk and loopy belief propagation mechanisms. We evaluate our framework on both synthetic and real-world network topologies, including a large-scale, labeled Twitter network comprising 20M nodes and 265M edges, and demonstrate that SYBILFUSE outperforms state-of-the-art approaches significantly. In particular, SYBILFUSE achieves 98% of Sybil coverage among top-ranked nodes.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Communications and Network Security, CNS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538645864
DOIs
StatePublished - Aug 10 2018
Event6th IEEE Conference on Communications and Network Security, CNS 2018 - Beijing, China
Duration: May 30 2018Jun 1 2018

Publication series

Name2018 IEEE Conference on Communications and Network Security, CNS 2018

Other

Other6th IEEE Conference on Communications and Network Security, CNS 2018
CountryChina
CityBeijing
Period5/30/186/1/18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

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