Enhanced ANN-Based Real-Time Secure Authentication and Data Sharing Framework for Cloud Platforms - Salakram Sharma (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In the evolving landscape of cloud computing, ensuring real-time secure authentication and privacy-preserving data sharing remains a critical challenge. This paper proposes a novel Artificial Neural Network (ANN)-driven framework that integrates multi-layer authentication with encrypted data exchange, tailored for leading cloud platforms such as AWS and Azure. The framework employs lightweight ANN classifiers trained on real-time user behavior datasets to perform continuous identity verification, while simultaneously securing data through white-box cryptographic primitives. Experimental results demonstrate high authentication accuracy (98.7%), low latency (1.2 ms average per transaction), and strong resilience against common attack vectors including replay and impersonation attacks. When benchmarked against traditional token-based and rule-based systems, our approach exhibits superior performance in terms of both security and computational efficiency. This work lays the foundation for deploying intelligent, responsive, and secure systems for real-time cloud-based data operations in sectors such as healthcare, finance, and government.