PCA-PSO-BP Neural Network Application in IDS
Lan Shi, YanLong Yang, JanHui Lv
Available Online May 2015.
- https://doi.org/10.2991/ipemec-15.2015.29How to use a DOI?
- BP neural network; particle swarm optimization; principal components analysis; intrusion detection
- 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.
- 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 SP - 145 EP - 150 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 -