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DEFAD: ensemble classifier for DDOS enabled flood attack defense in distributed network environment

Authors:

K. Munivara Prasad,

K. Venugopal RaoAuthors Info & Claims

Cluster Computing, Volume 21, Issue 4

Pages 1765 - 1783

https://doi.org/10.1007/s10586-018-2808-5

Published: 01 December 2018 Publication History

Abstract

Technological advancements in the information systems and networks are the outcome of potential developments resulting in the networking and communications. The role of Critical Infrastructure is playing a vital role in imparting the condition of effective information systems management. However, with some of the negative developments like DDoS attacks that impact the operations of network application systems, there are adverse set of issues encountered. With the rising number of DDoS attacks phenomenon, researchers have focused on developing contemporary solutions that can support in thwarting such attacks. From the review of such models in the literature review, it is imperative that two distinct dimensions like the detection and mitigation accuracy levels has scope for improvement and profoundly majority of such models were tested on the static datasets which are not pragmatic. Considering such equations, the model proposed in this manuscript focused on a contemporary range of solution that can be high on accuracy rate and also is tested over the dynamic dataset to understand the efficacy of the system. Using the ensemble classifiers comprising drift detection features, at service request stream level, the proposed solution if implemented can lead to better levels of detection. Experimental study of the model carried out using the service request stream that is synthesized is tested based on statistical metrics like accuracy, prediction value and true negative rates. Significance of the model is imperative in terms of results generated and its comparative analysis to the other bench-mark models in the segment.

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Cited By

Jyothsna VPrasad KRajiv KChandra G(2021)Flow based anomaly intrusion detection system using ensemble classifier with Feature Impact ScaleCluster Computing10.1007/s10586-021-03277-524:3(2461-2478)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s10586-021-03277-5
Lalropuia KKhaitan (nee Gupta) V(2021)Availability and reliability analysis of cloud computing under economic denial of sustainability (EDoS) attack: a semi-Markov approachCluster Computing10.1007/s10586-021-03257-924:3(2177-2191)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s10586-021-03257-9
Jain MKaur G(2021)Distributed anomaly detection using concept drift detection based hybrid ensemble techniques in streamed network dataCluster Computing10.1007/s10586-021-03249-924:3(2099-2114)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s10586-021-03249-9
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Published In

cover image Cluster Computing

Cluster Computing Volume 21, Issue 4

December 2018

266 pages

ISSN:1386-7857

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2018

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Cited By

Jyothsna VPrasad KRajiv KChandra G(2021)Flow based anomaly intrusion detection system using ensemble classifier with Feature Impact ScaleCluster Computing10.1007/s10586-021-03277-524:3(2461-2478)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s10586-021-03277-5
Lalropuia KKhaitan (nee Gupta) V(2021)Availability and reliability analysis of cloud computing under economic denial of sustainability (EDoS) attack: a semi-Markov approachCluster Computing10.1007/s10586-021-03257-924:3(2177-2191)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s10586-021-03257-9
Jain MKaur G(2021)Distributed anomaly detection using concept drift detection based hybrid ensemble techniques in streamed network dataCluster Computing10.1007/s10586-021-03249-924:3(2099-2114)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1007/s10586-021-03249-9
Bhuvaneswari Amma NSelvakumar S(2021)A statistical class center based triangle area vector method for detection of denial of service attacksCluster Computing10.1007/s10586-020-03120-324:1(393-415)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1007/s10586-020-03120-3

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