Ontology-Driven Cross-Domain Access Control Framework For Anomaly Detection In Cloud-Based Healthcare System

Abstract

To enhance anomaly detection in cloud-based healthcare systems, this study suggests a novel 
ontology-driven cross-domain access control architecture. The framework ensures data integrity 
and privacy by utilizing semantic technologies to offer safe, context-aware access across several 
healthcare domains. The system can dynamically detect anomalous access patterns and react to 
possible threats in real-time by incorporating machine learning-based anomaly detection 
techniques. Furthermore, dynamic policy enforcement guarantees that access controls are updated 
and modified regularly to address changing security threats. The capacity of the suggested 
framework to ensure adherence to crucial legal requirements like HIPAA and GDPR, promoting 
a safe and open access control environment, is one of its most notable aspects. In healthcare 
systems, where patient data is sensitive and needs to be shielded from unwanted access, this is 
essential. The framework can handle complicated, cross-domain healthcare data while 
guaranteeing interoperability across different systems thanks to the incorporation of semantic 
reasoning. The framework obtains a high resilience score of 94.1%, a low false-positive rate of 
3.4%, and an anomaly detection rate of 93.7%, according to performance metrics. These outcomes 
show how well the framework can detect and reduce security risks while preserving high 
efficiency. The system is a powerful option for dynamic and constantly evolving cloud healthcare 
environments because of its scalability, adaptability, and ability to safely manage complicated 
healthcare data. An efficient, scalable, and legal method for protecting cloud-based healthcare 
systems against changing cybersecurity threats is offered by this study.