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

TL;DR

This paper presents a hybrid deep learning model combining ResNet-1D, BiGRU, and Multi-Head Attention for accurate, real-time intrusion detection in Industrial IoT systems, demonstrating superior performance across multiple datasets.

Contribution

The study introduces a novel hybrid model integrating ResNet-1D, BiGRU, and MHA, optimized for real-time IIoT intrusion detection with improved accuracy and robustness.

Findings

Achieved 98.71% accuracy on EdgeHoTset dataset.

Reached 99.99% accuracy on CICIoV2024 dataset.

Demonstrated low inference latency suitable for real-time detection.

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.