Title:
ENHANCING HEALTHCARE SECURITY WITH CLOUD COMPUTING THREAT DETECTION AND ANOMALY MONITORING

Authors:
Vijaykumar Mamidala and Veerandra Kumar R, India

Abstract:
The integration of cloud computing and artificial intelligence (AI) in healthcare has revolutionized patient care and data management, yet it introduces significant cybersecurity vulnerabilities, particularly with the proliferation of Internet of Medical Things (IoMT) devices. This paper proposes an AI-driven framework leveraging Graph Neural Networks (GNNs) and Autoencoders for real-time threat detection and anomaly monitoring in cloud-based healthcare systems. By employing a multi-stage methodology data collection from IoMT devices, preprocessing, feature extraction, and GNN-based threat analysis the system achieves 92.5% accuracy, 91.0% precision, and 90.5% recall, demonstrating robust performance in identifying sophisticated cyber threats like DDoS attacks and ransomware. A cloud-based feedback mechanism ensures continuous adaptation to evolving threats while maintaining compliance with HIPAA and GDPR. The results validate the framework’s scalability, efficiency, and resilience, offering a viable solution to safeguard sensitive patient data in dynamic healthcare environments.