Abstract: The Industrial Internet of Things (IIoT) revolutionizes industrial operations by integrating smart sensors, advanced analytics, and machine learning to optimize manufacturing and supply chain processes. However, the rapid growth of IIoT presents tremendous cybersecurity challenges, as the increasing number of connected devices makes these systems more susceptible to cyberattacks. Intrusion detection systems (IDS) are vital for monitoring network and system activity to prevent and identify malicious attacks in IIoT. This paper discusses the role of AI- and computational-intelligence-driven IDS in protecting IIoT networks, addressing crucial challenges such as device heterogeneity, real-time operational limitations, and the mission-critical nature of industrial systems. Traditional IDS solutions are challenged by high false positives and dynamic cyber threats, and AI-based methods are a necessary evolution. Machine learning (ML) and artificial intelligence (AI) improve the effectiveness of IDS by enhancing anomaly detection, minimizing false alarms, and learning new attack patterns in dynamic IIoT environments. The paper discusses current IDS technologies, emphasizing their strengths and weaknesses in IIoT security. AI-based IDS utilizes deep learning, hybrid models, and feature engineering to improve accuracy and efficiency. Furthermore, future directions in industrial cybersecurity include federated learning for decentralized security, lightweight AI models for resource-scarce IIoT devices, and blockchain for secure data exchange. AI-based IDS can protect critical infrastructure and provide reliable, safe, and resilient industrial processes in IIoT by addressing these issues. The present study highlights the growing role of AI and computational intelligence in making industrial cybersecurity resilient against evolving threats.

Keywords: IIoT, Computational Intelligence, Artificial Intelligence, Machine Learning, Intrusion Detection System, Deep Learning

Pages: 393-412