The Internet of Things (IoT) and Industrial IoT (IIoT) have rapidly evolved, reshaping modern industries through intelligent automation and seamless real-time connectivity. Additionally, the inherent heterogeneity, limited resources, and distributed structure of a network has made them vulnerable. Those issues are used by the cyber attacker to gain an unauthorized access to the system, including data leakage, insider misuse, and Distributed Denial of Service (DDoS) attacks. To counter these security risks, we work to introduce a novel model ASTRID-Net, an innovative deep learning architecture building a Triple attention hybrid model that includes multi-scale convolutional feature extraction, bidirectional recurrent modeling, and Residual learning for high-accuracy intrusion detection in IoT and IIoT networks. The framework integrates multi-scale Convolutional Neural Networks (CNNs) to extract spatial features, Bidirectional Gated Recurrent Units (BiGRUs) to capture temporal relationships, and a combined channel-temporal attention mechanism to prioritize the most relevant information in the data. Experimental evaluation reveals that ASTRID-Net attains an outstanding 99.97% accuracy, with macro-averaged precision, recall, and F1-score surpassing 99.97%, outperforming conventional deep learning baselines. These results confirm the effectiveness and scalability of ASTRID-Net for real-time detection of complex cyber threats in IoT/IIoT infrastructures, contributing to the development of secure and adaptive cyber-physical systems.
Zannat et al. (Wed,) studied this question.