Abstract
The rapid expansion of Internet of Things (IoT) and Industrial Internet of Things (IIoT) networks has significantly increased the vulnerabilities of critical infrastructures to cyberattacks, posing substantial risks to both security and operational integrity. As these networks continue to grow, traditional intrusion detection systems (IDS) often fail to handle the massive volume, diversity, and sophistication of emerging threats, necessitating the development of more advanced solutions. This study introduces TACNet, a novel deep learning framework designed to enhance intrusion detection in IoT and IIoT environments. The primary objective of this work is to develop a robust model that not only detects a wide range of cyber threats but also adapts to the dynamic nature of these networks. The proposed TACNet architecture combines multi-scale Convolutional Neural Networks (CNN) for feature extraction at various granularities, Long Short-Term Memory (LSTM) networks to capture temporal dependencies in sequential network traffic, and temporal attention mechanisms to focus the model’s learning on the most informative time steps and features. This hybrid approach effectively addresses the challenges of both spatial and temporal data in network traffic, significantly improving model accuracy and interpretability. Experimental results demonstrate the effectiveness of TACNet, achieving accuracy rates from 98.56% to 99.98% on diverse datasets, including CICIDS 2018, DNN-EdgeIIoT, CIC IoT-DIAD 2024, TabularIoTAttack-2024, and N-BaIoT. These findings highlight superior performance of TACNet compared to traditional machine learning-based models, positioning it as a powerful solution for real-time intrusion detection in IoT and IIoT networks.