Advancing IoMT Security: Machine Learning-Based Detection and Classification of Multi-Protocol Cyberattacks

The increasing vulnerabilities in the Internet of Medical Things (IoMT) necessitate advanced cyberattack detection and classification mechanisms, particularly multi-protocol attacks that compromise device functionality and patient safety. The study addresses the lacuna in detecting multi-protocol cyberattacks in IoMT networks by developing a new dataset and applying machine learning algorithms to improve detection efficiency. The study employs a robust research design with simulated network traffic to train Random Forests (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The dataset spans a diverse range of attack scenarios to permit extensive feature engineering and performance measurements with precision, recall, and F1 scores. The results indicate that the RF model is more accurate in detecting complex attacks like U2R with a score of 0.74. Despite challenges in differentiating some attack categories, the study highlights the SVM model's potential for handling ambiguous cases. This work contributes a foundational dataset and taxonomy for future research, allowing for enhanced security testing of IoMT devices. It emphasizes the need for interdisciplinary collaboration to develop scalable, resource-efficient, and context-aware solutions. Practical implications include real-time intrusion detection for hospital networks, while limitations, such as reliance on synthetic data, underscore the need for broader dataset diversity. The findings underscore the critical importance of proactive measures to mitigate evolving threats, safeguard IoMT environments, and ensure the privacy and security of sensitive medical data. This research sets the stage for future advancements in IoMT security, integrating adaptive machine-learning techniques to address emerging cyber threats effectively.