Abstract
Due to the drastic increase in the number of critical infrastructures like nuclear plants, industrial control systems (ICS), transportation, it becomes highly vulnerable to several attacks. They become the major targets of cyberattacks due to the increase in number of interconnections with other networks. Several research works have focused on the design of intrusion detection systems (IDS) using machine learning (ML) and deep learning (DL) models. At the same time, Blockchain (BC) technology can be applied to improve the security level. In order to resolve the security issues that exist in the critical infrastructures and ICS, this study designs a novel BC with deep learning empowered cyber-attack detection (BDLE-CAD) in critical infrastructures and ICS. The proposed BDLE-CAD technique aims to identify the existence of intrusions in the network. In addition, the presented enhanced chimp optimization based feature selection (ECOA-FS) technique is applied for the selection of optimal subset of features. Moreover, the optimal deep neural network (DNN) with search and rescue (SAR) optimizer is applied for the detection and classification of intrusions. Furthermore, a BC enabled integrity checking scheme (BEICS) has been presented to defend against the misrouting attacks. The experimental result analysis of the BDLE-CAD technique takes place and the results are inspected under varying aspects. The simulation analysis pointed out the supremacy of the BDLE-CAD technique over the recent state of art techniques with the accuy of 92.63%.