Clustering-Driven Intelligent Trust Management Methodology for the Internet of Things (CITM-IoT)

Alshehri, MD; Hussain, FK; Hussain, OK

Clustering-Driven Intelligent Trust Management Methodology for the Internet of Things (CITM-IoT)

Alshehri, MD

Hussain, FK

Hussain, OK

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Publication Type:: Journal Article
Citation:: Mobile Networks and Applications, 2018, 23 (3), pp. 419 - 431
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Alshehri, MD https://orcid.org/0000-0001-9520-330X	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Hussain, OK	en_US
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	Mobile Networks and Applications, 2018, 23 (3), pp. 419 - 431	en_US
dc.identifier.issn	1383-469X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125669
dc.description.abstract	© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The growth and adoption of the Internet of Things (IoT) is increasing day by day. The large number of IoT devices increases the risk of security threats such as (but not limited to) viruses or cyber-attacks. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. Trust management and trust assessment has been extensively studied in distributed networks in general and the IoT in particular, but there are still outstanding pressing issues such as bad-mouthing of trust values which prevent them from being used in practical IoT applications. Furthermore, there is no research in ensuring that the developed IoT trust solutions are scalable across billions of IoT nodes. To address the above-mentioned issues, we propose a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.	en_US
dc.relation.ispartof	Mobile Networks and Applications	en_US
dc.relation.isbasedon	10.1007/s11036-018-1017-z	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Clustering-Driven Intelligent Trust Management Methodology for the Internet of Things (CITM-IoT)	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	23	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0999 Other 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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The growth and adoption of the Internet of Things (IoT) is increasing day by day. The large number of IoT devices increases the risk of security threats such as (but not limited to) viruses or cyber-attacks. One possible approach to achieve IoT security is to enable a trustworthy IoT environment in IoT wherein the interactions are based on the trust value of the communicating nodes. Trust management and trust assessment has been extensively studied in distributed networks in general and the IoT in particular, but there are still outstanding pressing issues such as bad-mouthing of trust values which prevent them from being used in practical IoT applications. Furthermore, there is no research in ensuring that the developed IoT trust solutions are scalable across billions of IoT nodes. To address the above-mentioned issues, we propose a methodology for scalable trust management solution in the IoT. The methodology addresses practical and pressing issues related to IoT trust management such as trust-based IoT clustering, intelligent methods for countering bad-mouthing attacks on trust systems, issues of memory-efficient trust computation and trust-based migration of IoT nodes from one cluster to another. Experimental results demonstrate the effectiveness of the proposed approaches.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122296