SVM Algorithm Training for DDoS on SDN Networks

Murtadha; Shujairiand; Škorpil, Vladislav

SVM Algorithm Training for DDoS on SDN Networks

Files

eeict-general-475-479.pdf(740.67 KB)

Date

2022

Authors

Murtadha

Shujairiand

Škorpil, Vladislav

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Abstract

Despite the flexibility provided by SDN technology is also vulnerable to attacks such as DDoS attacks, Network DDoS attack is a serious threat to the Internet today because internet traffic is increasing day by day, it is difficult to distinguish between legitimate and malicious traffic. To alleviate the DDoS attack in the campus network, to mitigate this attack, propose in this paper to classify benign traffic from DDoS attack traffic by SVM of the classification algorithms based on machine learning. As the contribution of this paper is to train the SVM algorithmwhich has been used in the approach for the training process. Due to the complexity of the dataset, using a type of kernel called a polynomial kernel to accomplish non-linearity discriminative. The results showed that the traffic classification was with the highest accuracy 96 %.

Keywords

SDN, ML, SVM, RYU, DdoS

Citation

Proceedings I of the 28st Conference STUDENT EEICT 2022: General papers. s. 475-479. ISBN 978-80-214-6029-4
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence