DDoS Detection in SDN Switches using Support Vector Machine Classifier

DOI

10.2991/jimet-15.2015.63How to use a DOI?

Keywords

SDN Switches, Distributed Denial-of-Service (DDoS), Support Vector Machine (SVM), Genetic Algorithm (GA)

Abstract

Compared with traditional network, Software Defined network (SDN) technology contains data plane, control plane and application plane. The control plane centralized controls multiple switches instead of only one switch. Therefore, SDN has more security requirements. The existing network security equipment already can no longer adapt to the environment of SDN. Distributed Denial-of-Service Attacks (DDoS) is one of the most major threats. DDoS detection is necessary for SDN switches. Support vector machine (SVM) classification technology is widely used in various fields. In this article, we will detect DDoS attacks using SVM optimized parameter c and g with cross validation-genetic algorithm (CV-GA). The experiments show that CV-GA-SVM classification performs better than others.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xue Li
AU  - Dongming Yuan
AU  - Hefei Hu
AU  - Jing Ran
AU  - Shulan Li
PY  - 2015/12
DA  - 2015/12
TI  - DDoS Detection in SDN Switches using Support Vector Machine Classifier
BT  - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference
PB  - Atlantis Press
SP  - 344
EP  - 348
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimet-15.2015.63
DO  - 10.2991/jimet-15.2015.63
ID  - Li2015/12
ER  -