SACFFNet: Shuffle Attention Convolutional Forward Fractional Network Based Attack Detection in Cloud Computing

Cloud computing is the massive evolution in Information Technology (IT) that provides virtualized and scalable resources to an end-user with minimum maintenance and cost. However, this environment is vulnerable to various attacks. These attacks impose heavy damages and affect the performance of the cloud. Therefore, timely detection of attacks in cloud computing is crucial. Hence, an efficient detection model is required for identifying various attacks in cloud computing. In this research, Shuffle Attention Convolutional Forward Fractional Network (SACFFNet) is presented to detect attacks in cloud computing. Initially, the cloud is simulated and next, a recorded log file is obtained from certain datasets. Thereafter, feature scaling is accomplished employing the minimum-maximum method. Then, features are selected using chord distance and Support Vector Machine Recursive Feature Elimination (SVM-RFE). Afterwards, data augmentation is done utilizing Synthetic Minority Oversampling Technique (SMOTE). Lastly, attacks are detected employing the newly designed SACFFNet. The SACFFNet is designed by integrating Shuffle Attention Network (SA-Net) with Convolutional Neural Network (CNN) with the modification of layers based on Fractional Calculus (FC). Additionally, SACFFNet has attained 91.678% of accuracy, 92.884% of sensitivity, and 92.090% of specificity.