Research on Property and Model Optimization of Multiclass SVM for NIDS

DOI

10.2991/isccca.2013.155How to use a DOI?

Keywords

IDS, SVM, online detection, rate of False-Negatives, rate of False-Positives

Abstract

By investigating insufficiency of typical artificial intelligence algorithms aiming at the high rate of False-Positives and False-Negatives in the Intrusion Detection Systems (IDS), this paper presents an approach that Support Vector Machine(SVM) is embedded in Network Intrusion Detection System (NIDS). At the same time, by using online data and K-fold cross-validation method, this paper proposes a method to optimize the attributes and model of SVM respectively. Experimental results show that by using this method as the detection core of the intrusion detection system, the rate of False-Negatives in IDS can be reduced significantly.

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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TY  - CONF
AU  - Jianhao Song
AU  - Gang Zhao
AU  - Junyi Song
PY  - 2013/02
DA  - 2013/02
TI  - Research on Property and Model Optimization of Multiclass SVM for NIDS
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)
PB  - Atlantis Press
SP  - 616
EP  - 619
SN  - 1951-6851
UR  - https://doi.org/10.2991/isccca.2013.155
DO  - 10.2991/isccca.2013.155
ID  - Song2013/02
ER  -