Social Turing Tests: Crowdsourcing Sybil Detection

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dc.contributor.authorWang, Gang Alanen
dc.contributor.authorMohanlal, Manishen
dc.contributor.authorWilson, Christoen
dc.contributor.authorWang, Xiaoen
dc.contributor.authorMetzger, Miriamen
dc.contributor.authorZheng, Haitaoen
dc.contributor.authorZhao, Ben Y.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2018-05-31T14:37:49Zen
dc.date.available2018-05-31T14:37:49Zen
dc.date.issued2013-02en
dc.description.abstractAs 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.en
dc.identifier.urihttp://hdl.handle.net/10919/83429en
dc.language.isoen_USen
dc.publisherInternet Societyen
dc.relation.ispartofNDSS Symposium 2013en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleSocial Turing Tests: Crowdsourcing Sybil Detectionen
dc.typeConference proceedingen
dc.typePresentationen

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Scholarly Works, Computer Science