Аннотация
The incorporation of the Internet of Things (IoT) within the healthcare sector significantly improves operational efficiency, elevates patient care, and increases cost-effectiveness. Nonetheless, it introduces obstacles including data security, interoperability, and ethical considerations. Devices connected through the Internet of Things gather health data from patients and send it to servers. Here, machine learning algorithms analyze the information to identify abnormal vital signs and enable early detection of diseases. Considering the vulnerability of these devices to cyber threats, implementing robust security measures is essential. The study utilizes the WUSTL EHMS 2020 dataset, integrating patient biometrics with network flow data, to create machine learning algorithms aimed at detecting atypical network traffic associated with IoT medical devices. The dataset is subjected to preprocessing to establish balanced supervised models, which encompass Support Vector Machine (SVM), Naive Bayes, Random Forest and Logistic Regression. The Random Forest model demonstrated an impressive 100% accuracy and an ideal F1 score, establishing it as the optimal selection for real-time IoT security applications. Naive Bayes and SVM demonstrated commendable performance, yet their accuracy and recall rates fell short. Logistic Regression, although beneficial, encountered challenges with false positives and false negatives. The results highlight the significance of choosing the right model in IoT security, with Random Forest emerging as the most dependable approach for ensuring consistent threat detection.