Hybrid Deep Learning Enabled Intrusion Detection in Clustered IIoT Environment

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.