Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013)

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Analysis about Performance of Multiclass SVM Applying in IDS

Authors
Gang Zhao, Jianhao Song, Junyi Song
Corresponding Author
Gang Zhao
Available Online March 2013.
DOI
10.2991/icibet.2013.46How to use a DOI?
Abstract

This paper presents a novel network in-trusion detection approach with the Sup-port Vector Machine embedded in and K-fold cross-validation method compound-ed for optimizing the attributes and SVM model. Compared with some representa-tive machine learning method, online data experimental results show that this method can be used to reduce the rate of False-Negatives in the intrusion detection system.

Copyright
© 2013, 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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Volume Title
Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013)
Series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-57-4
ISSN
1951-6851
DOI
10.2991/icibet.2013.46How to use a DOI?
Copyright
© 2013, 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/).

Cite this article

risenwbib
TY  - CONF
AU  - Gang Zhao
AU  - Jianhao Song
AU  - Junyi Song
PY  - 2013/03
DA  - 2013/03
TI  - Analysis about Performance of Multiclass SVM Applying in IDS
BT  - Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013)
PB  - Atlantis Press
SP  - 213
EP  - 218
SN  - 1951-6851
UR  - https://doi.org/10.2991/icibet.2013.46
DO  - 10.2991/icibet.2013.46
ID  - Zhao2013/03
ER  -