Advanced Machine Learning Architecture for Smart Healthcare Cybersecurity and Intelligent Cloud Ecosystems
DOI:
https://doi.org/10.15662/IJARCST.2026.0902009Keywords:
Machine Learning, Smart Healthcare, Cybersecurity, Cloud Computing, Federated Learning, Intrusion Detection, Artificial Intelligence, Edge Computing, Data Privacy, Intelligent SystemsAbstract
The rapid digital transformation of healthcare systems has introduced unprecedented opportunities for intelligent care delivery while simultaneously exposing critical vulnerabilities in cybersecurity and cloud infrastructure. This paper proposes an advanced machine learning (ML) architecture designed to enhance cybersecurity resilience in smart healthcare environments and optimize intelligent cloud ecosystem management. The architecture integrates deep learning, federated learning, anomaly detection, and reinforcement learning to ensure secure, scalable, and privacy-preserving healthcare data processing. By leveraging distributed cloud-edge intelligence, the proposed system enables real-time threat detection, adaptive access control, and predictive risk assessment across heterogeneous healthcare networks. The framework emphasizes privacy preservation through federated learning, allowing decentralized model training without exposing sensitive patient data. Additionally, AI-driven intrusion detection systems (IDS) and behavioral analytics are incorporated to identify abnormal activities in IoT-enabled medical devices and cloud services. The proposed architecture also supports intelligent workload orchestration across hybrid cloud environments, ensuring efficiency and resilience. Simulation-based evaluation indicates improved threat detection accuracy, reduced response latency, and enhanced system reliability compared to traditional security frameworks. The study highlights the importance of integrating machine learning with cybersecurity protocols to build trustworthy, intelligent healthcare ecosystems capable of adapting to evolving cyber threats while maintaining compliance with data protection regulations
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