Secure IIoT-Enabled Industry 4.0

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent thre...

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Autores principales:	Zeeshan Hussain, Adnan Akhunzada, Javed Iqbal, Iram Bibi, Abdullah Gani
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Industrial Internet of Things Internet-of-Things network security deep learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/c490b44f9df0428583efd43d18308fb8
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spelling	oai:doaj.org-article:c490b44f9df0428583efd43d18308fb82021-11-25T19:00:37ZSecure IIoT-Enabled Industry 4.010.3390/su1322123842071-1050https://doaj.org/article/c490b44f9df0428583efd43d18308fb82021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12384https://doaj.org/toc/2071-1050The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance.Zeeshan HussainAdnan AkhunzadaJaved IqbalIram BibiAbdullah GaniMDPI AGarticleIndustrial Internet of ThingsInternet-of-Thingsnetwork securitydeep learningEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12384, p 12384 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Industrial Internet of Things Internet-of-Things network security deep learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	Industrial Internet of Things Internet-of-Things network security deep learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Zeeshan Hussain Adnan Akhunzada Javed Iqbal Iram Bibi Abdullah Gani Secure IIoT-Enabled Industry 4.0
description	The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance.
format	article
author	Zeeshan Hussain Adnan Akhunzada Javed Iqbal Iram Bibi Abdullah Gani
author_facet	Zeeshan Hussain Adnan Akhunzada Javed Iqbal Iram Bibi Abdullah Gani
author_sort	Zeeshan Hussain
title	Secure IIoT-Enabled Industry 4.0
title_short	Secure IIoT-Enabled Industry 4.0
title_full	Secure IIoT-Enabled Industry 4.0
title_fullStr	Secure IIoT-Enabled Industry 4.0
title_full_unstemmed	Secure IIoT-Enabled Industry 4.0
title_sort	secure iiot-enabled industry 4.0
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c490b44f9df0428583efd43d18308fb8
work_keys_str_mv	AT zeeshanhussain secureiiotenabledindustry40 AT adnanakhunzada secureiiotenabledindustry40 AT javediqbal secureiiotenabledindustry40 AT irambibi secureiiotenabledindustry40 AT abdullahgani secureiiotenabledindustry40
_version_	1718410429428924416

Secure IIoT-Enabled Industry 4.0

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