融合BiGRU、LSTM与注意力机制，提升医疗与工业物联网的跨域入侵检测能力
A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security
- 采用双向门控单元、LSTM与多头注意力融合架构，捕捉时序依赖与上下文特征
- 在两个数据集上准确率分别达99.13%和99.34%，单次推理仅需0.0001~0.0002秒
- 适合部署于异构物联网环境，误报率低，兼具高精度与实时性
随着医疗物联网（IoMT）与工业物联网（IIoT）互联性增强，网络安全挑战日益复杂，敏感数据、患者安全及工业运营面临高级威胁。为应对风险，本文提出一种新型基于Transformer的入侵检测系统（IDS），命名为BiGAT-ID。该模型融合双向门控循环单元（BiGRU）、长短期记忆网络（LSTM）与多头注意力机制（MHA），有效捕捉双向时序依赖关系，建模序列模式并增强上下文特征表示。在两个基准数据集——CICIoMT2024（医疗物联网）与EdgeIIoTset（工业物联网）上的大量实验表明，该模型具备优异的跨域鲁棒性，检测准确率分别达到99.13%和99.34%。同时，模型展现出极高的运行效率，单实例推理时间在IoMT场景下低至0.0002秒，在IIoT场景下低至0.0001秒。结合低误报率，验证了BiGAT-ID在真实异构物联网环境中可靠且高效的部署潜力。
原文摘要 · Abstract (English)
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
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