Resisting Sybil Attack By Social Network and Network Clustering

Xu, Ling; Satayapiwat, C.; Takizawa, Hiroyuki; Kobayashi, Hiroaki

doi:10.1109/saint.2010.32

Cited by 12 publications

(12 citation statements)

References 14 publications

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“…In the evaluation SybilInfer handled networks with up to 30K nodes, which is much smaller than the size of regular online social networks. The algorithm proposed by Xu et al [18] calculates the shortest path between every pair of nodes within the network in each round, which makes it impractical for even small-sized social networks. In contrast, SybilDefender only relies on performing a limited number of random walks in the social graph, and it is scalable to large networks.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, there has been an increasing interest in defending against sybil attacks in social networks [6], [12], [18], [19], [20]. In a social network, two user identities share a link if a relationship is established between them.…”

Section: Introductionmentioning

confidence: 99%

“…The large sizes of these networks require that any scheme aiming to defend against sybil attacks in online social networks should be efficient and scalable. Some previous schemes can achieve good performance on a very small network sample (2000 nodes in [18] and 30000 nodes in [6]), but their algorithms are computationally intensive and cannot scale to networks with millions of nodes. For the schemes that performed evaluation on million-node samples of online social networks, SybilGuard [20] admits O( √ n log n) sybil nodes per attack edge, where n is the number of honest nodes; SybilLimit [19] improves over SybilGuard by accepting O(log n) sybil nodes per attack edge, but it is still away from the theoretical lower bound by a log n factor.…”

Section: Introductionmentioning

confidence: 99%

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SybilDefender: Defend against sybil attacks in large social networks

Wei

Tan

et al. 2012

2012 Proceedings IEEE INFOCOM

109

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Abstract-Distributed systems without trusted identities are particularly vulnerable to sybil attacks, where an adversary creates multiple bogus identities to compromise the running of the system. This paper presents SybilDefender, a sybil defense mechanism that leverages the network topologies to defend against sybil attacks in social networks. Based on performing a limited number of random walks within the social graphs, SybilDefender is efficient and scalable to large social networks. Our experiments on two 3,000,000 node real-world social topologies show that SybilDefender outperforms the state of the art by one to two orders of magnitude in both accuracy and running time. SybilDefender can effectively identify the sybil nodes and detect the sybil community around a sybil node, even when the number of sybil nodes introduced by each attack edge is close to the theoretically detectable lower bound. Besides, we propose two approaches to limiting the number of attack edges in online social networks. The survey results of our Facebook application show that the assumption made by previous work that all the relationships in social networks are trusted does not apply to online social networks, and it is feasible to limit the number of attack edges in online social networks by relationship rating.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SybilDefender: Defend against sybil attacks in large social networks

Wei

Tan

et al. 2012

2012 Proceedings IEEE INFOCOM

109

View full text Add to dashboard Cite

show abstract

“…Recently, there has been an increasing interest in defending against sybil attacks in social networks [3], [4], [5], [6], [7]. In a social network, two user identities share a link if a relationship is established between them.…”

mentioning

confidence: 99%

“…The large sizes of these networks require that any scheme aiming to defend against sybil attacks in online social networks should be efficient and scalable. Some previous schemes can achieve good performance on a very small network sample (2,000 nodes in [5] and 30,000 nodes in [3]), but their algorithms are computationally intensive and cannot scale to networks with millions of nodes. For the schemes that performed evaluation on million-node samples of online social networks, SybilGuard [7] admits Oð ffiffiffi n p log nÞ sybil nodes per attack edge, where n is the number of honest nodes; SybilLimit [6] improves over SybilGuard by accepting Oðlog nÞ sybil nodes per attack edge, but it is still away from the theoretical lower bound by a log n factor.…”

mentioning

confidence: 99%

SybilDefender: A Defense Mechanism for Sybil Attacks in Large Social Networks

Wei

Tan

et al. 2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Distributed systems without trusted identities are particularly vulnerable to sybil attacks, where an adversary creates multiple bogus identities to compromise the running of the system. This paper presents SybilDefender, a sybil defense mechanism that leverages the network topologies to defend against sybil attacks in social networks. Based on performing a limited number of random walks within the social graphs, SybilDefender is efficient and scalable to large social networks. Our experiments on two 3,000,000 node real-world social topologies show that SybilDefender outperforms the state of the art by more than 10 times in both accuracy and running time. SybilDefender can effectively identify the sybil nodes and detect the sybil community around a sybil node, even when the number of sybil nodes introduced by each attack edge is close to the theoretically detectable lower bound. Besides, we propose two approaches to limiting the number of attack edges in online social networks. The survey results of our Facebook application show that the assumption made by previous work that all the relationships in social networks are trusted does not apply to online social networks, and it is feasible to limit the number of attack edges in online social networks by relationship rating.

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Privacy and security in online social networks: A survey

Kayes¹,

Iamnitchi

2017

Online Social Networks and Media

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Online Social Networks (OSN) are a permanent presence in today's personal and professional lives of a huge segment of the population, with direct consequences to offline activities. Built on a foundation of trust-users connect to other users with common interests or overlapping personal trajectories-online social networks and the associated applications extract an unprecedented volume of personal information. Unsurprisingly, serious privacy and security risks emerged, positioning themselves along two main types of attacks: attacks that exploit the implicit trust embedded in declared social relationships; and attacks that harvest user's personal information for ill-intended use. This article provides an overview of the privacy and security issues that emerged so far in OSNs. We introduce a taxonomy of privacy and security attacks in OSNs, we overview existing solutions to mitigate those attacks, and outline challenges still to overcome.

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Resisting Sybil Attack By Social Network and Network Clustering

Cited by 12 publications

References 14 publications

SybilDefender: Defend against sybil attacks in large social networks

SybilDefender: Defend against sybil attacks in large social networks

SybilDefender: A Defense Mechanism for Sybil Attacks in Large Social Networks

Privacy and security in online social networks: A survey

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