Context-Aware Threat Detection in Cloud-Native Healthcare Applications Using Federated Transfer Learning and Runtime Telemetry
Keywords:
Cloud-native healthcare, threat detection, federated learning, transfer learning, runtime telemetry, data privacy, anomaly detectionAbstract
The rapid digitization of healthcare services, supported by cloud-native architectures, has introduced significant challenges concerning security and patient data privacy. Traditional threat detection techniques often fall short in dynamic and distributed cloud environments due to their centralized data processing models. This research proposes a context-aware threat detection framework leveraging Federated Transfer Learning (FTL) and runtime telemetry to identify security anomalies in real-time without compromising patient data locality or compliance.
The proposed system is evaluated using real-world telemetry logs and synthetic attack vectors in a simulated healthcare cloud environment. The results demonstrate a 23% improvement in detection precision over baseline models while maintaining data privacy. Additionally, runtime telemetry adds contextual relevance to model predictions, improving overall system resilience. This approach offers a scalable and privacy-preserving threat detection method suitable for regulatory-compliant healthcare systems.
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