Deep Siamese domain adaptation convolutional neural network-based quaternion fractional order Meixner moments fostered big data analytical method for enhancing cloud data security.

In this paper, Quaternion Fractional Order Meixner Moments-based Deep Siamese Domain Adaptation Convolutional Neural Network-based Big Data Analytical Technique is proposed for improving Cloud Data Security (DSDA-CNN-QFOMM-BD-CDS). The proposed methodology comprises six phases: data collection, transmission, pre-processing, storage, analysis, and security of data. Big data analysis methodologies start with the data collection phase. Deep Siamese domain adaptation convolutional Neural Network (DSDA-CNN) is applied to categorize the types of attacks in the cloud database during the data analysis process. During data security phase, Quaternion Fractional Order Meixner Moments (QFOMM) is employed to protect the cloud data for encryption with decryption. The proposed method is implemented in JAVA and assessed using performance metrics, including precision, sensitivity, accuracy, recall, specificity, f-measure, computational complexity information loss, compression ratio, throughput, encryption time, decryption time. The performance of the proposed method offers 23.31%, 15.64%, 18.89% better accuracy and 36.69%, 17.25%, 19.96% less information loss. When compared to existing methods like Fractional order discrete Tchebyshev encryption fostered big data analytical model to maximize the safety of cloud data depend on Enhanced Elman spike neural network (EESNN-FrDTM-BD-CDS), an innovative scheme architecture for safe authentication along data sharing in cloud enabled Big data Environment (LZMA-DBSCAN-BD-CDS).