Advanced Security Framework for Patient Monitoring Systems: Integrating Machine Learning and Encryption for Enhanced Data Protection

This research focuses on enhancing the security of patient monitoring systems by implementing a dual-layer encryption approach and an advanced anomaly detection mechanism. the dual-layer encryption involves first encrypting patient data using the advanced encryption standard (aes) and then hashing the encrypted data with the secure hash algorithm 256 (sha-256). this method ensures robust protection of sensitive information both at rest and in transit. additionally, we employ various machine learning algorithms for real-time anomaly detection, and monitoring system activities to promptly identify and mitigate potential security threats. the experimental results demonstrate that the proposed framework effectively secures patient data while maintaining high accuracy in detecting anomalies, contributing to the overall integrity and reliability of digital healthcare solutions.