Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environments

Abstract

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.

References in corpus (6)

• Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
• Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey
• Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions
• Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey
• Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models
• Multi-view Correlation-aware Network Traffic Detection on Flow Hypergraph