Secure IIoT-Enabled Industry 4.0

Zeeshan Hussain, Adnan Akhunzada (), Javed Iqbal, Iram Bibi and Abdullah Gani
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Zeeshan Hussain: Computer Science Department, Comsats University, Islamabad 45550, Pakistan
Javed Iqbal: Computer Science Department, Comsats University, Islamabad 45550, Pakistan
Iram Bibi: Centre for Security, Reliability and Trust, University of Luxembourg, L-4365 Luxembourg, Luxembourg
Abdullah Gani: Faculty of Computing and Informatics, University Malaysia Sabah, Labuan 88400, Malaysia

Sustainability, 2021, vol. 13, issue 22, 1-14

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.

Keywords: Industrial Internet of Things; Internet-of-Things; network security; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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