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.