International Journal of Networked and Distributed Computing

Volume 8, Issue 2, March 2020, Pages 86 - 93

Analysis of Features Dataset for DDoS Detection by using ASVM Method on Software Defined Networking

Authors
Myo Myint Oo, Sinchai Kamolphiwong, Thossaporn Kamolphiwong*, Sangsuree Vasupongayya
Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University (Hatyai), Hatyai, Songkhla 90110, Thailand
*Corresponding author. Email: kthossaporn@coe.psu.ac.th
Corresponding Author
Thossaporn Kamolphiwong
Received 29 June 2019, Accepted 1 September 2019, Available Online 9 April 2020.
DOI
https://doi.org/10.2991/ijndc.k.200325.001How to use a DOI?
Keywords
cross-validation, distributed denial of service, performance evaluation, software defined networking, support vector machine
Abstract

The impact of Distributed Denial of Service (DDoS) attack is one of the major concerns for Software Defined Networking (SDN) environments. Support Vector Machine (SVM) has been used in a DDoS attack detection mechanism on SDN. The advantages of SVM algorithms in DDoS attack detections are high accuracy and low false positive rate. However, SVM algorithm takes too long for training and testing time. A large number of literatures have been tried to get better results in a SVM-based DDoS attack detection. They proposed various kinds of SVM-based detection methods. Their results were measuring and evaluating by using various evaluation metrics. As a result, a SVM-based detection performance depends on the nature of traffic datasets. In this paper, our focus is to analyze the extracted features from the SDN traffics dataset resulting on a reduction of bias data from the dataset. SDN traffics features dataset were validated by using 10-fold cross-validation method. The effectiveness of our created dataset was validated by comparing with other dataset, e.g. Knowledge Discovery and Data Mining Tools Competition (KDDCUP) 99 dataset. In conclusion, our proposed dataset can be used effectively for SVM on SDN.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Networked and Distributed Computing
Volume-Issue
8 - 2
Pages
86 - 93
Publication Date
2020/04
ISSN (Online)
2211-7946
ISSN (Print)
2211-7938
DOI
https://doi.org/10.2991/ijndc.k.200325.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Myo Myint Oo
AU  - Sinchai Kamolphiwong
AU  - Thossaporn Kamolphiwong
AU  - Sangsuree Vasupongayya
PY  - 2020
DA  - 2020/04
TI  - Analysis of Features Dataset for DDoS Detection by using ASVM Method on Software Defined Networking
JO  - International Journal of Networked and Distributed Computing
SP  - 86
EP  - 93
VL  - 8
IS  - 2
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.k.200325.001
DO  - https://doi.org/10.2991/ijndc.k.200325.001
ID  - Oo2020
ER  -