REVIEW OF METHODS OF APPLYING MACHINE LEARNING TECHNOLOGY TO ENSURING INFORMATION SECURITY OF PERSONAL DATA IN MEDICAL SYSTEMS

Аннотация

This article explores the application of machine learning (ML) methods in the field of cybersecurity, with a focus on protecting sensitive data in medical information systems. As digitalization in healthcare accelerates, the volume and complexity of cyber threats also increase, demanding more intelligent and adaptive protection mechanisms. Machine learning offers advanced capabilities for detecting and neutralizing threats at an early stage through the automated analysis of large volumes of data. The proposed approach is based on the development of a framework that integrates various ML algorithms for dynamic threat identification. This system allows for real-time diagnostics and ensures high precision in detecting both known and emerging types of cyberattacks. The study highlights the importance of systematically collecting and analyzing statistical data to improve the prediction of potential vulnerabilities. Special attention is given to the application of machine learning in the medical sector, where data confidentiality is of critical importance. The integration of ML into healthcare information systems not only increases their security but also improves their adaptability and resilience. The findings of this study confirm that the use of modern algorithms significantly enhances the effectiveness of cybersecurity measures and helps maintain the privacy of medical records. Additionally, the study identifies several challenges and limitations, such as the need for algorithm adaptation to diverse operational conditions and the requirement for qualified personnel. Despite these challenges, machine learning remains a promising direction for improving the quality and reliability of data protection systems.The implementation of machine learning in medical cybersecurity opens up new opportunities for creating secure, intelligent, and adaptive systems that effectively respond to the growing number of threats in modern healthcare.