Cloud Data Protection Using Weibull Distributed Recurrent Neural Ergodic Signcryption

Cloud computing has become an integral part of modern computing, offering scalable storage and processing resources. However, the security of data stored in the cloud remains a major concern, especially when dealing with sensitive information. Traditional encryption schemes, while effective, often face limitations in terms of computational overhead and vulnerability to advanced attacks. To address these challenges, we propose a novel Weibull Distributed Recurrent Neural Ergodic Skewed Certificateless Signcryption scheme aimed at enhancing data protection in cloud environments. The key problem addressed by this work is the inherent inefficiency of existing cryptographic solutions that either rely on certificate-based systems or suffer from high computational and communication costs. This is especially crucial in cloud systems where real-time data processing is essential. Our approach integrates Weibull distribution for key management and optimization, recurrent neural networks (RNNs) for secure data transmission prediction, and ergodic skewed signcryption to eliminate the need for certificate authorities. This results in improved security, reduced computational overhead, and efficient communication, ensuring that the data remains secure even in dynamic cloud environments. The proposed scheme was tested using various metrics, including encryption/decryption time, data throughput, and attack resistance. Results demonstrate a significant reduction in computational cost by approximately 28% compared to traditional certificateless encryption. Furthermore, encryption times decreased from an average of 1.8 ms to 1.2 ms, and the scheme showed robustness against man-in-the-middle and chosen-ciphertext attacks with a detection accuracy of 98.6%. These results confirm the efficacy of the proposed system for enhancing security in cloud computing environments while maintaining high performance.