On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0

TL;DR

This paper evaluates the effectiveness of AI-based intrusion detection in Healthcare 5.0, highlighting the importance of biomedical data and explainability in cybersecurity for interconnected medical systems.

Contribution

It introduces an XAI approach combining network and biomedical data for intrusion detection, demonstrating high accuracy and interpretability in healthcare cybersecurity.

Findings

XGBoost achieved 99% F1-score for benign and data alteration attacks.

Biomedical features, especially temperature, contributed to spoofing detection.

Network data was the primary factor in intrusion detection.

Abstract

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.