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