cs 20(18): e6

Research Article

Adaptive Learning Method for DDoS Attacks on Software Defined Network Function Virtualization

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  • @ARTICLE{10.4108/eai.7-9-2020.166286,
    author={S. Janarthanam and N. Prakash and M. Shanthakumar},
    title={Adaptive Learning Method for DDoS Attacks on Software Defined Network Function Virtualization},
    journal={EAI Endorsed Transactions on Cloud Systems},
    volume={6},
    number={18},
    publisher={EAI},
    journal_a={CS},
    year={2020},
    month={9},
    keywords={Denial of Services, Software Defined Network, Support Vector Machine, Virtualization Functions, Networking},
    doi={10.4108/eai.7-9-2020.166286}
}
  • S. Janarthanam
    N. Prakash
    M. Shanthakumar
    Year: 2020
    Adaptive Learning Method for DDoS Attacks on Software Defined Network Function Virtualization
    CS
    EAI
    DOI: 10.4108/eai.7-9-2020.166286
S. Janarthanam1,*, N. Prakash1, M. Shanthakumar2
  • 1: Assistant Professor, Department of Computer Science, Gobi Arts & Science College, Gobichettipalayam, Erode, Tamilnadu, India
  • 2: Assistant Professor, Department of Computer Science, Kamban College of Arts & Science, Coimbatore, Tamilnadu, India
*Contact email: professorjana@gmail.com

Abstract

Software Defined Network (SDN) system controller stands with excessive benefits from the separated promoting devices. The SDN will resolve security issues, inheritance community with acute liabilities. The most important exposure is DDoS attack. The goals of this work to endorse a learning technique on DDoS attacks by SDN based system. Disturb the user’s defensible actions elevate to advise Adaptive Learning method (ALM) as advance set of SVM to return certain viabilities. This paper notices two types of flooding-based DDoS attacks. Proposed Virtualization method decreases the exercise and testing time using the key features, namely the volumetric and the asymmetric features. The accurateness of the revealing process is around 97% of fastest practice and investigation time.

Keywords
Denial of Services, Software Defined Network, Support Vector Machine, Virtualization Functions, Networking
Received
2020-04-16
Accepted
2020-09-01
Published
2020-09-07
Publisher
EAI
http://dx.doi.org/10.4108/eai.7-9-2020.166286

Copyright © S. Janarthanam et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.