Deep Transfer Learning for Secure Healthcare Modernization Quality-Assured Integrated Maintenance and Cyber-Threat Intelligence for Data-Scarce Regions

Abstract

Healthcare systems in data-scarce regions face dual challenges: limited annotated clinical data and rising cyber threats that compromise safety and continuity of care. This paper proposes a Deep Transfer Learning (DTL) framework that integrates quality-assured predictive maintenance for healthcare infrastructure with an embedded cyber-threat intelligence (CTI) module to secure medical devices and data flows. Our approach leverages domain-adaptive transfer learning, synthetic data augmentation, and federated learning to overcome limited local datasets while preserving patient privacy. The maintenance component uses pre-trained deep predictive models fine-tuned to local sensor signatures and operational logs, enabling proactive fault detection and reducing downtime. The CTI module employs transfer-learned representations to detect anomalous network behavior, adversarial probing, and device tampering, integrating with local incident response workflows. We evaluate the framework in simulated and small-scale pilot deployments across three representative clinical settings, demonstrating improvements in fault detection accuracy (average +18%), reduction in unscheduled downtime (median −27%), and early cyber intrusion detection (false positive rate reduced by 22%) compared to baseline local models. The paper discusses practical implementation considerations, governance and privacy-preserving mechanisms, and a roadmap for scalable adoption in resource-constrained health systems.