Author(s): Zhuhua Cai and Christopher Jermaine

Download: Paper (PDF)

Date: 7 Feb 2012

Document Type: Briefing Papers

Additional Documents: Slides

Associated Event: NDSS Symposium 2012


We propose a new statistical model and associated learning algorithm for detecting Sybil attacks in online social networks, which groups the nodes in the network into closely linked communities positioned in a latent Euclidean space. Our model outperforms state of the art algorithms in simulated attacks on real network topologies.