Title: An integrated principal component and reduced multivariate data analysis technique for detecting DDoS attacks in big data federated clouds

Authors: Sengathir Janakiraman

Addresses: Department of Information Technology, CVR College of Engineering, Vastunagar, Mangalpally, R.R District, Telangana-501 510, India

Abstract: The rapid development and wide application of cloud computing in the applications of big data on clouds necessitates the process of handling massive data, since they distributed among the diversely located data centre clouds. Thus, the need for an efficient detection scheme that differentiates legitimate cloud traffic from illegitimate becomes indispensable. In this paper, an integrated principal component and reduced multivariate data analysis (PCA-RMD) technique was proposed for detecting DDoS attacks in big data federated clouds. This proposed PCA-RMD initially reduces the dimension of feature characteristics extracted from the big data traffic information by minimising the principal components based on the method of correlation. Further, the correlation method is utilised for discriminating traffic based on enhanced and adaptive and multivariate correlation analysis (EAMCA) and enhanced Mahalanobis distance (EMD). The proposed PCA-RMD technique is predominant in classification accuracy, memory consumptions and CPU cost compared to the baseline approaches used for investigation.

Keywords: big data federated clouds; DDoS attacks; multivariate data analysis; principle component analysis; enhanced Mahalanobis distance; EMD.

DOI: 10.1504/IJCC.2021.119190

International Journal of Cloud Computing, 2021 Vol.10 No.4, pp.339 - 355

Received: 20 Jun 2019
Accepted: 21 Oct 2019
Published online: 29 Nov 2021 *

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