Secure IIoT-enabled industry 4.0

Zeeshan Hussain and Adnan Akhunzada and Javed Iqbal and Iram Bibi and Abdullah Gani (2021) Secure IIoT-enabled industry 4.0. Sustainability, 13. pp. 1-14. ISSN 2071-1050

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URL: https://www.mdpi.com/2071-1050/13/22/12384

Abstract

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

Item Type:	Article
Keyword:	Industrial Internet of Things , Internet-of-Things , Network security , Deep learning
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software T Technology > T Technology (General) > T1-995 Technology (General)
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	DG MASNIAH AHMAD -
Date Deposited:	21 Jul 2022 09:20
Last Modified:	21 Jul 2022 09:20
URI:	https://eprints.ums.edu.my/id/eprint/33421

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