Open Access

ARTICLE

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

by Anwer Mustafa Hilal^1,*, Jaber S. Alzahrani², Ibrahim Abunadi³, Nadhem Nemri⁴, Fahd N. Al-Wesabi^5,6, Abdelwahed Motwakel¹, Ishfaq Yaseen¹, Abu Sarwar Zamani¹

1 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
2 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
3 Department of Information Systems, Prince Sultan University, Riyadh, 11586, Saudi Arabia
4 Department of Information Systems, King King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
5 Department of Computer Science, King King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
6 Faculty of Computer and IT, Sana'a University, Sana'a, 61101, Yemen

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computers, Materials & Continua 2022, 72(1), 333-348. https://doi.org/10.32604/cmc.2022.024794

Received 31 October 2021; Accepted 20 December 2021; Issue published 24 February 2022

Abstract

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

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Keywords

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