FACVSPO: Fractional anti corona virus student psychology optimization enabled deep residual network and hybrid correlative feature selection for distributed denial-of-service attack detection in cloud using spark architecture

ABSTRACT

Cloud computing is an emerging standard in modern days for the purpose of sharing huge data, as it affords numerous user friendly behaviors. Cloud computing services offer an extensive range of resource pool in order to maintain huge scale data. Although, cloud computing model is disposed to several cyber-attacks and security problems regarding cloud structure, because of the dynamic and distribute character and exposures in virtualization implementation. Distributed denial-of-service (DDoS) attack is a type of cyber-attack, which disturbs the usual traffic of targeted cloud server. Moreover, DDoS produces malicious traffic in cloud structure, and thus consumes cloud resources. In this paper, an effective DDoS attack detection model, named fractional anti corona virus student psychology optimization-based deep residual network (FACVSPO-based DRN) is implemented using spark architecture. The devised FACVSPO approach is newly designed by incorporating anti coronavirus optimization (ACVO) algorithm, fractional calculus (FC) and student psychology based optimization (SPBO) model. Moreover, the hybrid correlative scheme is designed for extracting significant features for attack detection. The DRN structure is utilized for performing attack recognition, which categorizes the data as normal or attack. In addition, the DRN classifier is trained by the developed FACVSPO approach. The developed attack detection model outperformed other existing techniques in terms of testing accuracy, true negative rate (TNR), true positive rate (TPR) of 0.9236, 0.9141, and 0.9412, respectively. The testing accuracy of the implemented model is 12.02%, 8.92%, 7.27%, 6.30%, 5.68%, and 1.20% better than the existing methods, such as Taylor-elephant herd optimisation based deep belief network (TEHO-DBN), deep learning, deep neural network (DNN), multiple kernel learning, Fuzzy Taylor elephant herd optimisation (EHO)-based DBN, fractional anti corona virus optimization-deep neuro fuzzy network (FACVO-based DNFN), respectively. Similarly, the TNR is 10.14%, 6.88%, 5.94%, 5.46%, 4.25%, and 3.28% and TPR is 12.33%, 9.46%, 8.05%, 7.41%, 6.02%, and 3.04% better than the existing methods.