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

arXiv: https://arxiv.org/abs/2508.12470 · HTML: https://mlsanity.com/paper/2508.12470/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey](https://mlsanity.com/paper/2408.07583/) (2024)
- [Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems](https://mlsanity.com/paper/2405.18624/) (2024)
- [AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments](https://mlsanity.com/paper/2404.05602/) (2024)
- [GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models](https://mlsanity.com/paper/2409.02572/) (2024)
- [Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning](https://mlsanity.com/paper/2410.11463/) (2024)
- [FLoBC: A Decentralized Blockchain-Based Federated Learning Framework](https://mlsanity.com/paper/2112.11873/) (2021)
- [Recent Advances in the Internet of Medical Things (IoMT) Systems Security](https://mlsanity.com/paper/2302.04439/) (2023)
- [Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models](https://mlsanity.com/paper/2501.13962/) (2025)
- [Securing Healthcare with Deep Learning: A CNN-Based Model for medical IoT Threat Detection](https://mlsanity.com/paper/2410.23306/) (2024)
- [Multi-view Correlation-aware Network Traffic Detection on Flow Hypergraph](https://mlsanity.com/paper/2501.08610/) (2025)

## Built on by (most cited first, 2 of 2)

- [SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments](https://mlsanity.com/paper/2604.06254/) (2026)
- [Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environments](https://mlsanity.com/paper/2604.06481/) (2026)

## Abstract (arXiv)

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