Proceedings of the 2015 International conference on Applied Science and Engineering Innovation

Network Intrusion Detection Using Support Vector Machine Based on Particle Swarm Optimization

Authors
Li Wang, Chunhua Dong, Jianping Hu, Guodong Li
Corresponding Author
Li Wang
Available Online May 2015.
DOI
10.2991/asei-15.2015.125How to use a DOI?
Keywords
Network Intrusion Detection; Support Vector Machines (SVM); Particle Swarm Optimization (PSO); Multiclass Classification
Abstract

As an important part of the study of network security, Intrusion detection has aroused special attention of scholars from home and abroad. PSO-based SVM network intrusion detection is innovatively adopted in the paper where PSO is applied to support the parameters of SVM. Multi-classification is carried out with one versus one (OVO). The experiments on standard intrusion detection data set show that the PSO-based SVM method proposed in this paper is better than classical SVM method. Therefore, PSO -SVM test is very suitable for network intrusion detection.

Copyright
© 2015, 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 2015 International conference on Applied Science and Engineering Innovation
Series
Advances in Engineering Research
Publication Date
May 2015
ISBN
978-94-62520-94-3
ISSN
2352-5401
DOI
10.2991/asei-15.2015.125How to use a DOI?
Copyright
© 2015, 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

TY  - CONF
AU  - Li Wang
AU  - Chunhua Dong
AU  - Jianping Hu
AU  - Guodong Li
PY  - 2015/05
DA  - 2015/05
TI  - Network Intrusion Detection Using Support Vector Machine Based on Particle Swarm Optimization
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 665
EP  - 670
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.125
DO  - 10.2991/asei-15.2015.125
ID  - Wang2015/05
ER  -