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

2025-06-18

AI Summary

Healthcare 5.0’s hyperconnectivity intensifies cybersecurity threats, yet existing AI-based intrusion detection models largely neglect biomedical data, compromising detection efficacy and interpretability. To address this gap, we propose a novel multi-source heterogeneous modeling framework that jointly leverages network traffic and biomedical sensor data (e.g., body temperature). Our method employs XGBoost for classification and SHAP for post-hoc interpretability analysis. We are the first to systematically demonstrate the discriminative value of physiological signals—particularly temperature—in detecting medical cyberattacks. Results show that temperature exhibits high explanatory power for spoofing attacks (Shapley value = 0.37). On a hybrid dataset, the model achieves an F1-score of 99% for benign and data-tampering instances, and 81% for spoofing attacks. Network features provide robust general detection capability, while physiological features significantly enhance both interpretability and robustness for attack-specific identification.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Enhancing intrusion detection in Healthcare 5.0 using cyber-biomedical data
• Addressing neglect of biomedical data in AI-driven cybersecurity models
• Improving interpretability of intrusion detection with explainable AI (XAI)

Innovation

• XAI integrates network and biomedical data
• XGBoost achieves high F1-scores
• Biomedical features aid spoofing detection