Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers

Gupta, BB; Chaudhary, P; Chang, X; Nedjah, N

Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers

Gupta, BB Chaudhary, P Chang, X

Nedjah, N

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Computers and Electrical Engineering, 2022, 98
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Gupta, BB
dc.contributor.author	Chaudhary, P
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.contributor.author	Nedjah, N
dc.date.accessioned	2023-03-31T04:26:32Z
dc.date.available	2023-03-31T04:26:32Z
dc.date.issued	2022-03-01
dc.identifier.citation	Computers and Electrical Engineering, 2022, 98
dc.identifier.issn	0045-7906
dc.identifier.issn	1879-0755
dc.identifier.uri	http://hdl.handle.net/10453/168929
dc.description.abstract	From smart home to industrial automation to smart power grid, IoT- based solutions penetrate into every working field. These devices expand the attack surface and turned out to be an easy target for the attacker as resource constraint nature hinders the integration of heavy security solutions. Because IoT devices are less secured and operate mostly in unattended scenario, they perfectly justify the requirements of attacker to form botnet army to trigger Denial of Service attack on massive scale. Therefore, this paper presents a Machine Learning-based attack detection approach to identify the attack traffic in Consumer IoT (CIoT). This approach operates on local IoT network-specific attributes to empower low-cost machine learning classifiers to detect attack, at the local router. The experimental outcomes unveiled that the proposed approach achieved the highest accuracy of 0.99 which confirms that it is robust and reliable in IoT networks.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Computers and Electrical Engineering
dc.relation.isbasedon	10.1016/j.compeleceng.2022.107726
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers
dc.type	Journal Article
utslib.citation.volume	98
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-31T04:26:30Z
pubs.publication-status	Published
pubs.volume	98

Abstract:

From smart home to industrial automation to smart power grid, IoT- based solutions penetrate into every working field. These devices expand the attack surface and turned out to be an easy target for the attacker as resource constraint nature hinders the integration of heavy security solutions. Because IoT devices are less secured and operate mostly in unattended scenario, they perfectly justify the requirements of attacker to form botnet army to trigger Denial of Service attack on massive scale. Therefore, this paper presents a Machine Learning-based attack detection approach to identify the attack traffic in Consumer IoT (CIoT). This approach operates on local IoT network-specific attributes to empower low-cost machine learning classifiers to detect attack, at the local router. The experimental outcomes unveiled that the proposed approach achieved the highest accuracy of 0.99 which confirms that it is robust and reliable in IoT networks.

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

http://hdl.handle.net/10453/168929