Résumé:
The Internet of Medical Things (IoMT) revolutionizes healthcare by enabling real-time
monitoring and remote care but introduces significant security vulnerabilities. This thesis
proposes an intrusion detection system (IDS) using the XGBoost algorithm to address these
vulnerabilities. Utilizing the WUSTL-EHMS-2020 dataset, which integrates network flow
metrics and patient biometric data, our model demonstrates superior performance with
99.11% accuracy, 98.05% recall, and a prediction time of 0.02 seconds. Comparative analysis
with other machine learning models underscores the effectiveness of our approach. The
results highlight the potential of ensemble learning methods in enhancing IoMT security,
ensuring the protection of sensitive medical data and patient safety in interconnected
healthcare environments
