融合生物医学数据提升医疗物联网入侵检测效果
On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0
- 用可解释AI分析网络与生物传感器数据
- XGBoost模型对正常和篡改行为准确率达99%
- 体温特征对伪造攻击识别贡献显著
Healthcare 5.0通过人工智能、物联网、实时监测与以人为中心的设计，推动个性化医疗与预测诊断。然而，互联医疗设备日益暴露于网络威胁。当前基于AI的网络安全模型常忽略生物医学数据，影响检测效果与可解释性。本研究针对Healthcare 5.0数据集，融合网络流量与生物传感器数据，应用可解释人工智能（XAI）方法。分类结果显示，XGBoost模型对正常流量与数据篡改的F1-score达99%，对伪装攻击为81%。可解释性分析表明，网络数据在入侵检测中起主导作用，而生物医学特征有助于识别伪装攻击，其中体温特征的Shapley值高达0.37。
原文摘要 · Abstract (English)
Healthcare 5.0 integrates Artificial Intelligence (AI), the Internet of Things (IoT), real-time monitoring, and human-centered design toward personalized medicine and predictive diagnostics. However, the increasing reliance on interconnected medical technologies exposes them to cyber threats. Meanwhile, current AI-driven cybersecurity models often neglect biomedical data, limiting their effectiveness and interpretability. This study addresses this gap by applying eXplainable AI (XAI) to a Healthcare 5.0 dataset that integrates network traffic and biomedical sensor data. Classification outputs indicate that XGBoost achieved 99% F1-score for benign and data alteration, and 81% for spoofing. Explainability findings reveal that network data play a dominant role in intrusion detection whereas biomedical features contributed to spoofing detection, with temperature reaching a Shapley values magnitude of 0.37.
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