SybilLimit: A Near-Optimal Social Network Defense Against Sybil Attacks

By Haifeng Yu, Phillip B. Gibbons, Michael Kaminsky, Feng Xiao. 2010

270 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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Citations in the corpus

Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “Roughly speaking, there are two categories of graph-based classiﬁcation methods, i.e., collective classiﬁcation [1,9,10, 14,22,23,29,34,39,45,57,62–65,69,71] and graph neural network [6,36,50,66].”
Hang with your buddies to resist intersection attacks (2013, unknown): “To support open-ended groups, Buddies could build on one of the many Sybil attack resistance schemes, such as those based on social networks [48,54]—or could simply rate-limit Sybil attacks via some “barrier to entry,” e.”
Exploiting Temporal Dynamics in Sybil Defenses (2015, methodology): “SybilLimit does not assume knowledge of m ; the value of r = O ( √ m ) is estimated by the protocol using a benchmarking technique; please see [35] for more details.”
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, background): “SybilLimit [8] Sybil Detection Undirected Negative N S&P’08”
Defending against Sybil Devices in Crowdsourced Mapping Services (2015, background): “Compared to its counterparts (SybilGuard [53], SybilLimit [52] and SybilInfer [14]), SybilRank achieves higher accuracy at a lower computational cost.”
Sybil-Resilient Online Content Voting (2009, methodology): “Previous works have also exploited the use of trust networks to limit Sybil attacks [3,15,18,26,27,30], but none directly addresses the vote aggregation problem.”
CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks (2015, background): “Second, graph-based methods [9, 10, 13, 30, 36, 37] detect malicious accounts by using the observation that malicious accounts usually have few connections with normal accounts.”
A survey of attack and defense techniques for reputation systems (2009, background): “In followup work [Yu et al. 2008], the authors refine their previous graph-based solutions to significantly lower the bound on the number of malicious nodes in the system.”
SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, methodology): “For instance, random walk based approaches include SybilGuard [11], Sybil-Limit [12], SybilInfer [13], SybilRank [14], CIA [15], and SybilWalk [23].”