Open Access

ARTICLE

Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

Radwa Marzouk¹, Fadwa Alrowais², Noha Negm³, Mimouna Abdullah Alkhonaini⁴, Manar Ahmed Hamza^5,*, Mohammed Rizwanullah⁵, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵

1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha, 62529,Saudi Arabia
4 Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 72(2), 3763-3775. https://doi.org/10.32604/cmc.2022.027483

Received 18 January 2022; Accepted 20 February 2022; Issue published 29 March 2022

Abstract

Industrial Internet of Things (IIoT) is an emerging field which connects digital equipment as well as services to physical systems. Intrusion detection systems (IDS) can be designed to protect the system from intrusions or attacks. In this view, this paper presents a novel hybrid deep learning with metaheuristics enabled intrusion detection (HDL-MEID) technique for clustered IIoT environments. The HDL-MEID model mainly intends to organize the IIoT devices into clusters and enabled secure communication. Primarily, the HDL-MEID technique designs a new chaotic mayfly optimization (CMFO) based clustering approach for the effective choice of the Cluster Heads (CH) and organize clusters. Moreover, equilibrium optimizer with hybrid convolutional neural network long short-term memory (HCNN-LSTM) based classification model is derived to identify the existence of the intrusions in the IIoT environment. Extensive experimental analysis is performed to highlight the enhanced outcomes of the HDL-MEID technique and the results were investigated under different aspects. The experimental results highlight the supremacy of the proposed HDL-MEID technique over recent state-of-the-art techniques.

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Keywords

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