A GWO-optimized dual-attention CNN-LSTM model for robust IIoT intrusion detection

The rapid adoption of the Industrial Internet of Things (IIoT) in smart manufacturing and critical infrastructure has significantly increased the exposure of industrial networks to sophisticated cyber threats. Ensuring secure communication and reliable threat detection in IIoT environments has therefore become a critical challenge. This study proposes an intelligent Cyber Threat Detection and Response System that integrates a Hybrid Deep Neural Network with the Grey Wolf Optimizer to enhance security in IIoT networks. The proposed framework utilizes CyberTec IIoT Malware Dataset (CIMD‑2024) on Kaggle containing network traffic characteristics, device communication patterns, and anomaly indicators. A comprehensive data preprocessing phase is employed, including noise removal, normalization, and missing value handling, to improve data quality and model reliability. The hybrid deep learning architecture combines Convolutional Neural Networks for spatial feature extraction with Long Short-Term Memory networks to capture temporal dependencies in network behavior. Additionally, a dual-attention mechanism is incorporated to emphasize significant spatial and temporal features, thereby improving the accuracy of cyber threat classification. The Grey Wolf Optimizer is applied to optimize key hyperparameters such as learning rate, dropout rate, and batch size, leading to improved model performance. Experimental results demonstrate that the proposed model achieves an accuracy of 96.5%, outperforming several conventional machine learning approaches. Furthermore, improvements in recall and F1-score indicate enhanced capability in identifying diverse cyber threats within IIoT environments. The findings highlight the effectiveness of combining deep learning with bio-inspired optimization techniques to develop scalable and real-time cyber threat detection solutions for IIoT systems.