Efficient Machine Learning Frameworks for Strengthening Cybersecurity in Internet of Medical Things (IoMT) Ecosystems

As the Internet of Medical Things (IoMT) continues to transform healthcare, it also introduces new vulnerabilities to sophisticated cyberattacks that outpace conventional defenses. In response, we present a tailored Intrusion Detection System (IDS) optimized for IoMT environments, designed to operate within the constraints of resource-limited devices while addressing complex, real-world attack vectors. Leveraging the CICIoMT2024 dataset and advanced machine learning models like Random Forest and XGBoost, our approach overcomes severe class imbalance and high dimensionality. Using Recursive Feature Elimination with Cross-Validation (RFECV), we reduced the feature set by 44.45%, achieving a state-of-the-art weighted F1-score of 99.48%. Despite the superior performance of the Random Forest model, its large memory footprint poses challenges for deployment on IoMT devices with limited resources. In contrast, the XGBoost model offers a better balance between high detection accuracy and resource consumption, making it more suitable for real-world applications. Our solution offers a scalable, efficient, and deployable IDS that brings a new level of adaptability and precision to IoMT cybersecurity, ready to defend against today’s threats while evolving to meet tomorrow’s challenges.