On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0
Pedro H. Lui, Lucas P. Siqueira, Juliano F. Kazienko, Vagner E. Quincozes, Silvio E. Quincozes, Daniel Welfer
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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.