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

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/).

Download article (PDF)

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  -