healthMLsec: Machine Learning based Vulnerability Assessment in Health Systems: A Framework for Enhancing Cybersecurity and Patient Data

Abstract

In the advancing digital realm of healthcare, cyberattacks present substantial hazards to patient data integrity, privacy, including whole system security. As healthcare organizations increasingly depend on correlated digital platforms, it is essential to detect and prioritize vulnerabilities inside these systems to protect sensitive patient information. This study presents a machine learning based framework aimed at evaluating and mitigating cybersecurity risks in healthcare systems. The framework uses machine learning techniques to investigate extensive amounts of systems along with network data, detecting patterns that may indicate possible security vulnerabilities. The methodology facilitates the effective allocation of resources by prioritizing vulnerabilities according to their severity and probability of exploitation, allowing health organizations to concentrate on the most significant risks. Additionally, the proposed approach persistently adjusts to emerging threats by assimilating fresh data, confirming that the vulnerability analysis stays relevant among increasing cyber threats. This proactive strategy improves patient data security, cybersecurity against vulnerability, and complies with regulatory standards, hence reinforcing confidence in digital health systems. The study highlights the capability of machine learning to enhance cybersecurity resilience in healthcare, providing a strong, scalable approach for vulnerability evaluation and remediation with conclusion as well as future scope of the study.

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