Affiliations:
1Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
2Department of Computer Science, College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia
This paper presents a complete machine-learning framework for detecting and preventing cyberattacks in the healthcare sector. Because healthcare systems are highly vulnerable and data breaches can cause serious harm, the study seeks to address gaps in current solutions by developing an end-to-end model. Using a design science research approach, the framework includes five connected stages: data collection and preprocessing, data cleaning and feature selection, model training and evaluation, implementation and deployment, and continuous monitoring and improvement. The paper argues that this comprehensive approach, supported by comparisons with existing studies and an empirical analysis, offers a more effective and sustainable solution for healthcare cybersecurity than models that focus only on specific types of attacks.
Cybersecurity, Healthcare systems, Machine learning, Attack detection, Framework design
https://doi.org/10.21833/ijaas.2026.01.001
Alshammari, A., & Alqarni, A. (2026). Cyberattack detection and prevention framework for the healthcare sector using machine learning techniques. International Journal of Advanced and Applied Sciences, 13(1), 1–12. https://doi.org/10.21833/ijaas.2026.01.001