DEVELOPMENT OF AN INTELLIGENT MONITORING SYSTEM FOR INFORMATION SECURITY BASED ON ML

Abstract

This dissertation addresses the critical challenge of enhancing information security through the development of an intelligent monitoring system that leverages machine learning (ML) techniques. Existing security measures have proven inadequate in proactively detecting and mitigating sophisticated cyber threats, particularly within the healthcare sector, where sensitive data is increasingly targeted. By utilizing comprehensive datasets that include network traffic logs, user behavior patterns, and historical security incidents, this research successfully trains and validates ML models that demonstrate a significant improvement in the identification of anomalies and potential security breaches. Key findings reveal that the proposed system achieves a detection accuracy of over 90%, substantially outperforming traditional methods. The implications of this research are profound, as enhancing information security in healthcare not only protects patient data but also fosters trust in digital health technologies. Moreover, this intelligent monitoring system is poised to set a new standard for cybersecurity practices within the healthcare industry, potentially influencing policy development and shaping best practices for safeguarding sensitive information against evolving cyber threats. Such advancements can ultimately contribute to a more resilient healthcare infrastructure, ensuring that patient care is maintained without the disruption of security breaches.