An integrated principal component and reduced multivariate data analysis technique for detecting DDoS attacks in big data federated clouds Online publication date: Mon, 29-Nov-2021
by Sengathir Janakiraman
International Journal of Cloud Computing (IJCC), Vol. 10, No. 4, 2021
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Cloud Computing (IJCC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com