Social Turing Tests: Crowdsourcing Sybil Detection
Gang Wang
Manish Mohanlal
Christo Wilson
Xiao Wang
Miriam Metzger
Haitao Zheng
Ben Y. Zhao
The 20th Network & Distributed System Security Symposium (NDSS 2013)
[Full Text in PDF Format, 453KB]
Paper 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.