Publication detail

SVM Algorithm Training for DDoS on SDN Networks

SHUJAIRI, M. ŠKORPIL, V.

Original Title

SVM Algorithm Training for DDoS on SDN Networks

Type

conference paper

Language

English

Original 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 algorithm which 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

Authors

SHUJAIRI, M.; ŠKORPIL, V.

Released

26. 4. 2022

Publisher

Brno university of technology, Faculty of Electronic Engineering and Communication

Location

Brno

ISBN

978-80-214-6029-4

Book

Proceedings of the 28 Conference STUDENT EEICT 2022 General papers

Edition

1

Pages from

475

Pages to

479

Pages count

5

URL

BibTex

@inproceedings{BUT177743,
  author="Murtadha Mohsin Hadi {Shujairi} and Vladislav {Škorpil}",
  title="SVM Algorithm Training for DDoS on SDN Networks",
  booktitle="Proceedings of the 28 Conference STUDENT EEICT 2022 General papers",
  year="2022",
  series="1",
  pages="475--479",
  publisher="Brno university of technology, Faculty of Electronic Engineering and Communication",
  address="Brno",
  isbn="978-80-214-6029-4",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf"
}