A Comprehensive Review on Detection of DDoS Attacks using ML in SDN Environment

In technological facet, the ball game has been entirely changed due to virtualization. SDN is one of the applications of this virtualization. It is mostly used as well as famous network architecture in recent time. The data plane is separated from the Control network by using SDN or Software Defined...

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Bibliographic Details
Published in2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) pp. 1158 - 1163
Main Authors Gupta, Shaveta, Grover, Dinesh
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.03.2021
Subjects
Computer crime
DDoS
Denial-of-service attack
Machine learning
Servers
Software defined networking
Software Defined Networks
Telecommunication traffic
Virtualization
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Summary:In technological facet, the ball game has been entirely changed due to virtualization. SDN is one of the applications of this virtualization. It is mostly used as well as famous network architecture in recent time. The data plane is separated from the Control network by using SDN or Software Defined Network. Even though Software Defined Network offers a way which is easy and clear for network Control, but also it raised a new threat of security. These threats can be Denial-of-Service (DoS) attacks, Man in the middle attack and many others. DDoS is the most common and standard threat among this list. DDoS became it easier to begin the attack by affecting the server. Due to this effect, the entire network fails. Hence, it is crucial to detect this attack so that a server can work securely. This review paper compares the various machine learning techniques that are used in the detection of DDoS attacks in the SDN environment.
DOI:10.1109/ICAIS50930.2021.9395987