Reliability Analysis of Large-Scale Adaptive Weighted Networks

Song, B; Wang, X; Ni, W; Song, Y; Liu, RP; Jiang, GP; Guo, YJ

Reliability Analysis of Large-Scale Adaptive Weighted Networks

Song, B

Wang, X

Ni, W

Song, Y Liu, RP

Jiang, GP Guo, YJ

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Forensics and Security, 2020, 15 pp. 651 - 665
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Song, B https://orcid.org/0000-0002-2930-9752	en_US
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9439-6437	en_US
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.contributor.author	Song, Y	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Jiang, GP	en_US
dc.contributor.author	Guo, YJ https://orcid.org/0000-0001-6008-9682	en_US
dc.date.available	2021-01-01T18:03:17Z
dc.date.issued	2020-01-01	en_US
dc.identifier.citation	IEEE Transactions on Information Forensics and Security, 2020, 15 pp. 651 - 665	en_US
dc.identifier.issn	1556-6013	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134362
dc.description.abstract	© 2005-2012 IEEE. Disconnecting impaired or suspicious nodes and rewiring to those reliable, adaptive networks have the potential to inhibit cascading failures, such as DDoS attack and computer virus. The weights of disconnected links, indicating the workload of the links, can be transferred or redistributed to newly connected links to maintain network operations. Distinctively different from existing studies focused on adaptive unweighted networks, this paper presents a new mean-field model to analyze the reliability of adaptive weighted networks against cascading failures. By taking mean-field approximation, we develop a new continuous-time Markov model to capture the propagations of cascading failures and the rewiring actions that individual nodes can take to bypass failed neighbors. We analyze the stability of the model to identify the critical conditions, under which the cascading failures can be eventually inhibited or would proliferate. The conditions are evaluated under different link weight distributions and rewiring strategies. Our model reveals that preferentially disconnecting suspicious peers with high weights can effectively inhibit virus and failures.	en_US
dc.relation.ispartof	IEEE Transactions on Information Forensics and Security	en_US
dc.relation.isbasedon	10.1109/TIFS.2019.2926193	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Strategic, Defence & Security Studies	en_US
dc.title	Reliability Analysis of Large-Scale Adaptive Weighted Networks	en_US
dc.type	Journal Article
utslib.citation.volume	15	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

© 2005-2012 IEEE. Disconnecting impaired or suspicious nodes and rewiring to those reliable, adaptive networks have the potential to inhibit cascading failures, such as DDoS attack and computer virus. The weights of disconnected links, indicating the workload of the links, can be transferred or redistributed to newly connected links to maintain network operations. Distinctively different from existing studies focused on adaptive unweighted networks, this paper presents a new mean-field model to analyze the reliability of adaptive weighted networks against cascading failures. By taking mean-field approximation, we develop a new continuous-time Markov model to capture the propagations of cascading failures and the rewiring actions that individual nodes can take to bypass failed neighbors. We analyze the stability of the model to identify the critical conditions, under which the cascading failures can be eventually inhibited or would proliferate. The conditions are evaluated under different link weight distributions and rewiring strategies. Our model reveals that preferentially disconnecting suspicious peers with high weights can effectively inhibit virus and failures.

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http://hdl.handle.net/10453/134362