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This paper introduces a hybrid deep learning model for intrusion detection in IIoT, integrating ResNet-1D for spatial feature extraction, BiGRU for temporal dependencies, and Multi-Head Attention for feature weighting. The model was trained with SMOTE to address class imbalance in the EdgeHoTset dataset and evaluated on CICIoV2024. Results demonstrate superior performance compared to existing methods, achieving 98.71% and 99.99% accuracy on EdgeHoTset and CICIoV2024, respectively, with low inference latency, indicating its suitability for real-time applications.
A novel hybrid deep learning architecture achieves near-perfect accuracy and real-time performance in detecting cyberattacks in IIoT environments, outperforming existing methods.
This study introduces a hybrid deep learning model for intrusion detection in Industrial IoT (IIoT) systems, combining ResNet-1D, BiGRU, and Multi-Head Attention (MHA) for effective spatial-temporal feature extraction and attention-based feature weighting. To address class imbalance, SMOTE was applied during training on the EdgeHoTset dataset. The model achieved 98.71% accuracy, a loss of 0.0417%, and low inference latency (0.0001 sec /instance), demonstrating strong real-time capability. To assess generalizability, the model was also tested on the CICIoV2024 dataset, where it reached 99.99% accuracy and F1-score, with a loss of 0.0028, 0 % FPR, and 0.00014 sec/instance inference time. Across all metrics and datasets, the proposed model outperformed existing methods, confirming its robustness and effectiveness for real-time IoT intrusion detection.
