2012/05/17 by Gang Wang, Wang, Gang, Manish Mohanlal +11 · 2 citations
Computer Science · #FOS: Computer and information sciences #FOS: Physical sciences #Internet Traffic Analysis and Secure E-voting #Mobile Crowdsensing and Crowdsourcing #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Spam and Phishing Detection
paper · pdf · doi:10.48550/arxiv.1205.3856
openalex publication_date 2012/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.