Enhanced Elman spike neural network based fractional order discrete Tchebyshev encryption fostered big data analytical method for enhancing cloud data security

Cloud computing refers to a set of hardware and software that are connected to provide various computing services. Cloud consists of services to deliver software, infrastructure and platform over the internet depending on user demand. However, various categories of vulnerabilities and threats are increased, due to the improved demand and development of cloud computing. In cloud computing, data integrity and security are the main issues, which is reduced the system performance. Therefore, an Enhanced Elman Spike Neural Network (EESNN) based Fractional Order Discrete Tchebyshev moments (FrDTMs) encryption fostered big data analytical method is proposed in this manuscript for enhancing cloud data security. Initially, the input data is pre-processed using a Z-score normalization process. Also, the optimal features are implemented by Uni-variate ensemble feature selection technique, which can improve the quality and reliability of data. The proposed EESNN model is used to classify the attack types in cloud databases and the security is improved by FrDTMs model. The proposed EESNN-FrDTMs approach has achieved high data security and performance. The simulation of this model is done using JAVA. From the simulation, the proposed model attains 11.58%, 26.6%, 25.5%, 45.75%, 36.88% high accuracy, 7.37%, 15.43%, 8.68%, 11.42%, 16.88% lower information loss than the existing approaches, like PSO-PNN, eHIPF, GSNN-FEO, and BISNN-BO respectively.