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    Social Turing Tests: Crowdsourcing Sybil Detection

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    Date
    2013-02
    Author
    Wang, Gang Alan
    Mohanlal, Manish
    Wilson, Christo
    Wang, Xiao
    Metzger, Miriam
    Zheng, Haitao
    Zhao, Ben Y.
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    Abstract
    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 accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both “experts” and “turkers” under a variety of conditions, and find that while turkers vary significantly in their effectiveness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools.
    URI
    http://hdl.handle.net/10919/83429
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    • Destination Area: Integrated Security (IS) [119]
    • Scholarly Works, Computer Science [515]

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