A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security

2025-08-17

AI Summary

To address low detection accuracy, poor real-time performance, and insufficient robustness against advanced persistent threats (APTs) in cross-domain scenarios involving the Internet of Medical Things (IoMT) and the Industrial Internet of Things (IIoT), this paper proposes BiGAT-ID, a hybrid intrusion detection architecture. BiGAT-ID integrates bidirectional Gated Recurrent Units (BiGRU) for temporal modeling, Long Short-Term Memory (LSTM) networks for capturing long-range dependencies, and multi-head attention within a lightweight Transformer framework to enable adaptive focus on critical features. It is the first approach to simultaneously achieve high accuracy and ultra-low latency in IoMT/IIoT cross-domain security. Experiments on the CICIoMT2024 and EdgeIIoTset datasets yield detection accuracies of 99.13% and 99.34%, respectively, with an inference latency of only 0.0001 seconds per sample and significantly reduced false positive rates—demonstrating strong feasibility for edge deployment.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Biometrics, Face, Gesture & Pose

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The increased Internet of Medical Things IoMT and the Industrial Internet of Things IIoT interconnectivity has introduced complex cybersecurity challenges, exposing sensitive data, patient safety, and industrial operations to advanced cyber threats. To mitigate these risks, this paper introduces a novel transformer-based intrusion detection system IDS, termed BiGAT-ID a hybrid model that combines bidirectional gated recurrent units BiGRU, long short-term memory LSTM networks, and multi-head attention MHA. The proposed architecture is designed to effectively capture bidirectional temporal dependencies, model sequential patterns, and enhance contextual feature representation. Extensive experiments on two benchmark datasets, CICIoMT2024 medical IoT and EdgeIIoTset industrial IoT demonstrate the model's cross-domain robustness, achieving detection accuracies of 99.13 percent and 99.34 percent, respectively. Additionally, the model exhibits exceptional runtime efficiency, with inference times as low as 0.0002 seconds per instance in IoMT and 0.0001 seconds in IIoT scenarios. Coupled with a low false positive rate, BiGAT-ID proves to be a reliable and efficient IDS for deployment in real-world heterogeneous IoT environments

Problem

• Addressing cybersecurity challenges in medical and industrial IoT systems
• Detecting advanced cyber threats to protect sensitive data and operations
• Developing cross-domain intrusion detection with high accuracy and efficiency

Innovation

• Hybrid BiGRU-LSTM with attention mechanism
• Cross-domain robustness for IoT security
• Low-latency real-time intrusion detection