Cybersecure Cloud Computing for Banking and Healthcare: A Gradient-Boosting and ANN-Enhanced Framework Leveraging SAP and Oracle EBS Intelligence
DOI:
https://doi.org/10.15662/IJEETR.2025.0706003Keywords:
Cybersecure cloud computing, Gradient Boosting Machine (GBM), Artificial Neural Networks (ANN), SAP Intelligence, Oracle E-Business Suite (EBS), Banking security, Healthcare informatics, Federated learning, Zero-trust architecture, Regulatory compliance, Predictive analytics, Enterprise cloud integration, Data privacy, Fraud detection, Risk managementAbstract
The integration of advanced analytics and secure cloud architectures has become a critical enabler for digital transformation in data-sensitive industries such as banking and healthcare. This paper proposes a novel cybersecure cloud computing framework that synergistically combines Gradient Boosting Machine (GBM) algorithms and Artificial Neural Networks (ANNs) to enhance predictive intelligence and anomaly detection within enterprise environments powered by SAP and Oracle E-Business Suite (EBS) platforms. The proposed framework employs multi-layered encryption, federated learning, and zero-trust access controls to ensure compliance with regulatory mandates such as GDPR, HIPAA, and PCI DSS, while maintaining high scalability and interoperability across hybrid and multi-cloud infrastructures. Through real-time data pipelines and AI-driven orchestration, the system enhances fraud detection, risk management, and clinical decision support by leveraging integrated enterprise data streams. Experimental evaluations on synthetic and real-world banking and healthcare datasets demonstrate significant improvements in data confidentiality, processing latency, and predictive accuracy, outperforming traditional cloud security and analytics baselines. The results highlight the viability of combining intelligent analytics with robust cybersecurity principles for secure, efficient, and compliant cloud operations in mission-critical sectors.
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