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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
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf
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" }