Graph-based Sybil Detection in social and information systems

Yazan Boshmaf, Konstantin Beznosov, Matei Ripeanu

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

22 Citations (Scopus)

Abstract

Sybil attacks in social and information systems have serious security implications. Out of many defence schemes, Graph-based Sybil Detection (GSD) had the greatest attention by both academia and industry. Even though many GSD algorithms exist, there is no analytical framework to reason about their design, especially as they make different assumptions about the used adversary and graph models. In this paper, we bridge this knowledge gap and present a unified framework for systematic evaluation of GSD algorithms. We used this framework to show that GSD algorithms should be designed to find local community structures around known non-Sybil identities, while incrementally tracking changes in the graph as it evolves over time.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
PublisherAssociation for Computing Machinery
Pages466-473
Number of pages8
ISBN (Print)9781450322409
DOIs
Publication statusPublished - 1 Jan 2013
Event2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 - Niagara Falls, ON, Canada
Duration: 25 Aug 201328 Aug 2013

Publication series

NameProceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

Other

Other2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
CountryCanada
CityNiagara Falls, ON
Period25/8/1328/8/13

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Cite this

Boshmaf, Y., Beznosov, K., & Ripeanu, M. (2013). Graph-based Sybil Detection in social and information systems. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 (pp. 466-473). (Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013). Association for Computing Machinery. https://doi.org/10.1145/2492517.2492568