The Internet of Things (IoT) and the Industrial Internet of Things (IIoT) and their widespread application make them attractive targets for cyber attacks. Traditional cybersecurity methods such as firewalls and antivirus software are not always effective in protecting IoT/IIoT networks due to their heterogeneity and large number of connected devices. The zero-trust principle can be more effective in protecting IoT/IIoT networks. This principle assumes on no inherent trustworthiness of any user, device, or traffic, requiring authorization and verification before accessing any network resource. This article presents a zero-trust-based intrusion detection system (IDS) that uses machine learning to secure IoT/IIoT networks. The aim of this article is to develop a two-component IDS for detecting and classifying cyber-attacks. The proposed design for an Intrusion Detection System (IDS) achieves high accuracy in detecting attacks while maintaining optimal performance and minimizing additional computational costs. This is especially crucial for real-time network monitoring in IoT/IIoT environments.
