Machine Learning-Driven Threat Detection in Healthcare: A Cloud-Native Framework Using AWS Services - Venkata Jagadeesh Reddy Kopparthi, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
This article presents a comprehensive framework for implementing machine learning-based threat detection in healthcare organizations using AWS cloud services. The increasing sophistication of cyber threats in healthcare environments and stringent regulatory requirements for protecting patient data necessitate more advanced security solutions. The article proposes an intelligent threat detection system that leverages AWS services, including Amazon SageMaker, GuardDuty, and Macie, integrated with custom machine learning models for anomaly detection and predictive analysis. The article implements real-time monitoring capabilities for electronic health records (EHR), connected medical devices, and network activities while ensuring HIPAA compliance. The results demonstrate significant improvements in threat detection accuracy, reduced false positives, and enhanced response times compared to traditional security approaches. The system's ability to continuously learn from new data patterns and adapt to emerging threats showcases its effectiveness in maintaining robust healthcare cybersecurity. This article contributes to the growing body of knowledge in healthcare security and provides practical insights for organizations seeking to implement cloud-based machine learning solutions for proactive threat detection.
Keywords: Machine Learning; Healthcare Cybersecurity; Cloud Computing; Threat Detection; HIPAA Compliance