Proceedings of the 2015 International Power, Electronics and Materials Engineering Conference

PCA-PSO-BP Neural Network Application in IDS

Authors
Lan Shi, YanLong Yang, JanHui Lv
Corresponding Author
Lan Shi
Available Online May 2015.
DOI
https://doi.org/10.2991/ipemec-15.2015.29How to use a DOI?
Keywords
BP neural network; particle swarm optimization; principal components analysis; intrusion detection
Abstract
BP neural network has two disadvantages, one is to fall into local minimum value easily; the other is the slow convergence. We propose in this paper an approach, including three main operations. Firstly, the algorithm of particle swarm optimization (PSO) is applied to improve back propagation (BP) neural network. Secondly, principal components analysis (PCA) method is used to deal with the original information. Thirdly, after optimization of BP neural network, we employ it into the intrusion detection system. The simulation results reveal that the new proposed BP neural network is superior to the traditional BP neural network.
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Proceedings
2015 International Power, Electronics and Materials Engineering Conference
Part of series
Advances in Engineering Research
Publication Date
May 2015
ISBN
978-94-62520-73-8
ISSN
2352-5401
DOI
https://doi.org/10.2991/ipemec-15.2015.29How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Lan Shi
AU  - YanLong Yang
AU  - JanHui Lv
PY  - 2015/05
DA  - 2015/05
TI  - PCA-PSO-BP Neural Network Application in IDS
BT  - 2015 International Power, Electronics and Materials Engineering Conference
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/ipemec-15.2015.29
DO  - https://doi.org/10.2991/ipemec-15.2015.29
ID  - Shi2015/05
ER  -