Advanced Machine Learning-Driven Cyber Attack Detection in the Internet of Medical Things (IoMT) Ecosystem

Today, the rise of the Internet of Medical Things (IoMT) has evolved into a highly valued global market worth billions of dollars. However, this growth has also created many opportunities for massive and advanced attack scenarios due to the vast number of devices and their interconnected communication networks. Based on recent reports, it is observed that during the Covid-19 pandemic, the necessity of the IoMT ecosystem has increased significantly. On the other hand, attackers and intruders aim to impair data integrity and patient safety with the prevalence of sophisticated cyber attacks including Man in the Middle (MITM) attacks like spoofing and data injection. This research work uses the WUSTL-EHMS-2020 dataset to present a strong machine learning-based attack detection method. To tackle the distinct security issues of IoMT, we provide an ensemble model that combines Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) classifiers. By utilizing the complementing advantages of SVM's decision boundary precision and XGBoost's gradient-based optimization, our model outperforms baseline techniques with a superior detection accuracy of 94.29%. Further, it examines the applicability of a proposed model for IoMT-specific features, including heterogeneous medical device communication, resource limitations, and patient data sensitivity and compares the model with other IoT and IoMT datasets, revealing how cyberattack patterns vary based on environment. Thus this research work advances the creation of trustworthy and scalable cybersecurity solutions for Internet of Medical Things systems.