<![CDATA[Objective: The objective of this research is to propose a cyber-security risk assessment framework based on machine learning that can identify, assess and predict cyber risks in healthcare information systems, connected medical devices and intensive care unit (ICU) environments. Method: The research uses the IoT Healthcare Security Dataset (IoT-ICU HSD) consisting of normal and malicious network traffic generated from an IoT-enabled ICU setup, which consists of patient monitoring sensors and control units. A thorough data preprocessing pipeline is designed to enhance data quality and the performance of the model, which involves cleaning the data, feature engineering, feature selection and normalization. Various supervised machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, Logistic Regression and Extreme Gradient Boosting (XGBoost) are trained and tested for the classification of normal and malicious network traffic. The accuracy, precision, recall, F1-score, receiver operating characteristic area under the curve (ROC-AUC) and confusion matrix analysis are used for model performance assessment. Results: The results will provide actionable insight for healthcare organizations to enhance cybersecurity posture, secure connected medical devices, ensure uninterrupted critical care services and facilitate secure digital healthcare transformation. Novelty: The proposed framework seeks to offer early detection of cyber threats, facilitate proactive risk assessment, and boost health-care information system security and resilience while maintaining confidentiality, integrity, and availability of sensitive patient information.]]>
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