CST-AFNet: A dual attention-based deep learning framework for intrusion detection in IoT networks
Waqas Ishtiaq, Ashrafun Zannat, A. H. M. Shahariar Parvez, Md. Alamgir Hossain, Muntasir Hasan Kanchan, Muhammad Masud Tarek
Abstract
The rapid expansion of the Internet of Things (IoT) has revolutionized modern industries by enabling smart automation and real-time connectivity. However, this evolution has also introduced complex cybersecurity challenges due to the heterogeneous, resource-constrained, and distributed nature of these environments. To address these challenges, this research presents CST-AFNet, a novel dual attention-based deep learning framework specifically designed for robust intrusion detection in IoT networks. The model integrates multi-scale Convolutional Neural Networks (CNNs) for spatial feature extraction, Bidirectional Gated Recurrent Units (BiGRUs) for capturing temporal dependencies, and a dual attention mechanism, channel and temporal attention to enhance focus on critical patterns in the data. The proposed method was trained and evaluated on the Edge-IIoTset dataset, a comprehensive and realistic benchmark containing over 2.2 million labeled instances spanning 15 attack types and benign traffic, collected from a seven-layer industrial testbed. Our proposed model achieves an outstanding accuracy with 15 attack types and benign traffic. CST-AFNet model achieves 99.97% accuracy. Moreover, this model demonstrates an exceptional accuracy with macro-averaged precision, recall, and F1-score all above 99.3%. Experimental results demonstrate that CST-AFNet achieves superior detection accuracy, significantly outperforming traditional deep learning models. The findings confirm that CST-AFNet is a powerful and scalable solution for real-time cyber threat detection in complex IoT/IIoT environments, paving the way for more secure, intelligent, and adaptive cyber-physical systems.