Corpus ID: 2749452

Social Turing Tests: Crowdsourcing Sybil Detection

@article{Wang2013SocialTT,
  title={Social Turing Tests: Crowdsourcing Sybil Detection},
  author={G. Wang and M. Mohanlal and Christo Wilson and Xiao Wang and M. J. Metzger and H. Zheng and B. Zhao},
  journal={ArXiv},
  year={2013},
  volume={abs/1205.3856}
}
  • G. Wang, M. Mohanlal, +4 authors B. Zhao
  • Published 2013
  • Computer Science, Physics
  • ArXiv
  • As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effectively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explore the feasibility of a crowdsourced Sybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today's Sybil… CONTINUE READING
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