Enterprise-Grade Secure API Management using Deep Learning in Healthcare Cloud Environments
DOI:
https://doi.org/10.15662/IJEETR.2022.0405006Keywords:
Healthcare Cloud Security, API Management, Deep Learning, Zero-Trust Architecture, Federated Learning, EHR Protection, Threat Detection, Cybersecurity Compliance, HIPAA, GDPRAbstract
The rapid digitization of healthcare services and the widespread adoption of cloud computing have significantly increased the reliance on Application Programming Interfaces (APIs) for interoperability, data exchange, and service integration. However, the sensitive nature of electronic health records (EHRs), telemedicine systems, and connected medical devices makes healthcare APIs a prime target for cyberattacks. Traditional rule-based API security mechanisms are increasingly insufficient against advanced persistent threats, zero-day vulnerabilities, and sophisticated API abuse patterns. This paper proposes an enterprise-grade secure API management framework that integrates deep learning techniques to enhance threat detection, anomaly recognition, and adaptive access control in healthcare cloud environments. Leveraging architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models, the framework enables real-time behavioral analytics and predictive threat mitigation. The study examines compliance requirements under Health Insurance Portability and Accountability Act and General Data Protection Regulation while addressing scalability, latency, and model interpretability challenges. The proposed methodology combines API gateway monitoring, federated learning for privacy-preserving training, and zero-trust security principles to ensure confidentiality, integrity, and availability. Experimental evaluations demonstrate improved threat detection accuracy and reduced false positives compared to conventional security models, highlighting the transformative potential of deep learning in healthcare API governance
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