Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems

Recently, artificial intelligence (AI) has gained an abundance of attention in cybersecurity for Industry 4.0 and has shown immense benefits in a large number of applications. AI technologies have paved the way for multiscale security and privacy in cybersecurity, namely AI-based malicious intruder protection, AI-based intrusion detection, prediction, and classification, and so on. Moreover, AI-based techniques have a remarkable potential to address the challenges of cybersecurity that Industry 4.0 faces, which is otherwise called the IIoT. This manuscript concentrates on the design of the Golden Jackal Optimization with Deep Learning-based Cyberattack Detection and Classification (GJODL-CADC) method in the IIoT platform. The major objective of the GJODL-CADC system lies in the detection and classification of cyberattacks on the IoT platform. To obtain this, the GJODL-CADC algorithm presents a new GJO-based feature selection approach to improve classification accuracy. Next, the GJODL-CADC method makes use of a hybrid autoencoder-based deep belief network (AE-DBN) approach for cyberattack detection. The effectiveness of the AE-DBN approach can be improved through the design of the pelican optimization algorithm (POA), which in turn improves the detection rate. An extensive set of simulations were accomplished to demonstrate the superior outcomes of the GJODL-CADC technique. An extensive analysis highlighted the promising performance of the GJODL-CADC technique compared to existing techniques.