000000284 001__ 284
000000284 005__ 20130611092200.0
000000284 037__ $$aLERSSE-RefConfPaper-2013-001
000000284 100__ $$aYazan Boshmaf
000000284 245__ $$aGraph-based Sybil Detection in Social and Information Systems
000000284 260__ $$c2013-05-23
000000284 300__ $$amult. p
000000284 520__ $$aSybil attacks in social and information systems have serious security implications. Out of many defence schemes, Graph-based Sybil Detection (GSD) had the greatest attention by both academia and industry. Even though many GSD algorithms exist, there is no analytical framework to reason about their design, especially as they make different assumptions about the used adversary and graph models. In this paper, we bridge this knowledge gap and present a unified framework for systematic evaluation of GSD algorithms. We used this framework to show that GSD algorithms should be designed to find local community structures around known non-Sybil identities, while incrementally tracking changes in the graph as it evolves over time.
000000284 6531_ $$aSybil attack
000000284 6531_ $$aOnline Social Networks
000000284 6531_ $$aGraph-based Sybil Detection
000000284 6531_ $$aGraph Theory
000000284 6531_ $$aCommunity Detection
000000284 700__ $$aKonstantin Beznosov
000000284 700__ $$aMatei Ripeanu
000000284 8560_ $$fboshmaf@ece.ubc.ca
000000284 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/284/files/ASONAM_2013.pdf$$zRevision 2
000000284 909C4 $$pYazan Boshmaf, Konstantin Beznosov, Matei Ripeanu. Graph-based Sybil Detection in Social and Information Systems. In the Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'13), Niagara Falls, Canada, August 25-28, 2013.
000000284 980__ $$aRefConfPaper