Secure Healthcare Access Control System (SHACS) for Anomaly Detection and Enhanced Security in Cloud-Based Healthcare Applications

Abstract

The rapid adoption of cloud-based healthcare systems has intensified the need for secure, efficient, and adaptive access control mechanisms to protect sensitive Electronic Health Records (EHRs). This study presents an enhanced implementation of the Secure Healthcare Access Control System (SHACS), integrating a hybrid access control model with practical system deployment using Java-based architecture. Unlike traditional approaches that rely solely on Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC), the proposed system combines both models with dynamic rule enforcement to enable fine-grained and context-aware authorization. The framework incorporates multi-factor authentication, secure key exchange, and advanced encryption techniques to ensure data confidentiality during transmission and storage. Additionally, a real-time anomaly detection module analyzes user behavior patterns to identify and mitigate unauthorized access attempts proactively. The system is further strengthened through audit logging, data masking, and continuous monitoring, enhancing transparency and compliance with healthcare data protection standards. Experimental evaluation demonstrates improved authentication efficiency, reduced access latency, and enhanced scalability compared to conventional models. By providing a practical and deployable solution, the proposed system bridges the gap between theoretical security frameworks and realworld healthcare applications, ensuring robust protection of medical data while maintaining operational efficiency