PCA-PSO-BP Neural Network Application in IDS

DOI

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.

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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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  - Proceedings of the 2015 International Power, Electronics and Materials Engineering Conference
PB  - Atlantis Press
SP  - 145
EP  - 150
SN  - 2352-5401
UR  - https://doi.org/10.2991/ipemec-15.2015.29
DO  - 10.2991/ipemec-15.2015.29
ID  - Shi2015/05
ER  -