On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0
It addresses cybersecurity for interconnected medical technologies, but is incremental as it integrates existing methods with new data.
This study tackled the problem of cyber threats in Healthcare 5.0 by applying eXplainable AI to a dataset combining network traffic and biomedical sensor data, achieving up to 99% F1-score for benign and data alteration detection and 81% for spoofing detection.
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.