A Network Clustering Algorithm for Sybil-Attack Resisting

Ling XU; Ryusuke EGAWA; Hiroyuki TAKIZAWA; Hiroaki KOBAYASHI

doi:10.1587/transinf.E94.D.2345

A Network Clustering Algorithm for Sybil-Attack Resisting

Ling XU, Ryusuke EGAWA, Hiroyuki TAKIZAWA, Hiroaki KOBAYASHI

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The social network model has been regarded as a promising mechanism to defend against Sybil attack. This model assumes that honest peers and Sybil peers are connected by only a small number of attack edges. Detection of the attack edges plays a key role in restraining the power of Sybil peers. In this paper, an attack-resisting, distributed algorithm, named Random walk and Social network model-based clustering (RSC), is proposed to detect the attack edges. In RSC, peers disseminate random walk packets to each other. For each edge, the number of times that the packets pass this edge reflects the betweenness of this edge. RSC observes that the betweennesses of attack edges are higher than those of the non-attack edges. In this way, the attack edges can be identified. To show the effectiveness of RSC, RSC is integrated into an existing social network model-based algorithm called SOHL. The results of simulations with real world social network datasets show that RSC remarkably improves the performance of SOHL.

Publication: IEICE TRANSACTIONS on Information Vol.E94-D No.12 pp.2345-2352

Publication Date: 2011/12/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E94.D.2345

Type of Manuscript: Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)

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Ling XU, Ryusuke EGAWA, Hiroyuki TAKIZAWA, Hiroaki KOBAYASHI, "A Network Clustering Algorithm for Sybil-Attack Resisting" in IEICE TRANSACTIONS on Information, vol. E94-D, no. 12, pp. 2345-2352, December 2011, doi: 10.1587/transinf.E94.D.2345.
Abstract: The social network model has been regarded as a promising mechanism to defend against Sybil attack. This model assumes that honest peers and Sybil peers are connected by only a small number of attack edges. Detection of the attack edges plays a key role in restraining the power of Sybil peers. In this paper, an attack-resisting, distributed algorithm, named Random walk and Social network model-based clustering (RSC), is proposed to detect the attack edges. In RSC, peers disseminate random walk packets to each other. For each edge, the number of times that the packets pass this edge reflects the betweenness of this edge. RSC observes that the betweennesses of attack edges are higher than those of the non-attack edges. In this way, the attack edges can be identified. To show the effectiveness of RSC, RSC is integrated into an existing social network model-based algorithm called SOHL. The results of simulations with real world social network datasets show that RSC remarkably improves the performance of SOHL.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.E94.D.2345/_p

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@ARTICLE{e94-d_12_2345,
author={Ling XU, Ryusuke EGAWA, Hiroyuki TAKIZAWA, Hiroaki KOBAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={A Network Clustering Algorithm for Sybil-Attack Resisting},
year={2011},
volume={E94-D},
number={12},
pages={2345-2352},
abstract={The social network model has been regarded as a promising mechanism to defend against Sybil attack. This model assumes that honest peers and Sybil peers are connected by only a small number of attack edges. Detection of the attack edges plays a key role in restraining the power of Sybil peers. In this paper, an attack-resisting, distributed algorithm, named Random walk and Social network model-based clustering (RSC), is proposed to detect the attack edges. In RSC, peers disseminate random walk packets to each other. For each edge, the number of times that the packets pass this edge reflects the betweenness of this edge. RSC observes that the betweennesses of attack edges are higher than those of the non-attack edges. In this way, the attack edges can be identified. To show the effectiveness of RSC, RSC is integrated into an existing social network model-based algorithm called SOHL. The results of simulations with real world social network datasets show that RSC remarkably improves the performance of SOHL.},
keywords={},
doi={10.1587/transinf.E94.D.2345},
ISSN={1745-1361},
month={December},}

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TY - JOUR
TI - A Network Clustering Algorithm for Sybil-Attack Resisting
T2 - IEICE TRANSACTIONS on Information
SP - 2345
EP - 2352
AU - Ling XU
AU - Ryusuke EGAWA
AU - Hiroyuki TAKIZAWA
AU - Hiroaki KOBAYASHI
PY - 2011
DO - 10.1587/transinf.E94.D.2345
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2011
AB - The social network model has been regarded as a promising mechanism to defend against Sybil attack. This model assumes that honest peers and Sybil peers are connected by only a small number of attack edges. Detection of the attack edges plays a key role in restraining the power of Sybil peers. In this paper, an attack-resisting, distributed algorithm, named Random walk and Social network model-based clustering (RSC), is proposed to detect the attack edges. In RSC, peers disseminate random walk packets to each other. For each edge, the number of times that the packets pass this edge reflects the betweenness of this edge. RSC observes that the betweennesses of attack edges are higher than those of the non-attack edges. In this way, the attack edges can be identified. To show the effectiveness of RSC, RSC is integrated into an existing social network model-based algorithm called SOHL. The results of simulations with real world social network datasets show that RSC remarkably improves the performance of SOHL.
ER -