Automated Threat Detection in Healthcare APIs Using Deep Neural Networks within DevSecOps Frameworks
DOI:
https://doi.org/10.15662/IJRAI.2022.0506026Keywords:
Healthcare APIs, Deep Neural Networks, DevSecOps, Automated Threat Detection, Cloud Security, FHIR, Zero Trust, API Security, AI in CybersecurityAbstract
The rapid digitization of healthcare systems and the proliferation of cloud-native architectures have significantly increased reliance on interoperable healthcare APIs. Standards such as Health Level Seven International’s FHIR enable seamless data exchange among electronic health records (EHRs), telemedicine platforms, insurance systems, and mobile health applications. However, this interconnected ecosystem expands the attack surface, exposing healthcare APIs to sophisticated cyber threats including API abuse, credential stuffing, data exfiltration, and ransomware. Traditional rule-based security mechanisms struggle to detect zero-day and evolving attacks in dynamic cloud environments.
This research proposes an automated threat detection framework leveraging Deep Neural Networks (DNNs) integrated within DevSecOps pipelines for healthcare APIs. The framework embeds AI-driven static code analysis, runtime anomaly detection, behavioral analytics, and adaptive response mechanisms into continuous integration and continuous deployment (CI/CD) workflows. By applying deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders to API traffic and system logs, the system enables proactive detection of anomalous patterns and emerging threats. The study outlines architectural design, implementation strategy, evaluation metrics, and operational implications. Results demonstrate that DNN-based detection significantly enhances early threat identification, reduces response time, and strengthens compliance in cloud-based healthcare infrastructures.
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