Research on Computer Network Security Evaluation Based on Neural Network

DOI

10.2991/macmc-17.2018.125How to use a DOI?

Keywords

PSO, Computer network security, Neural network, Evaluation

Abstract

This paper studies the computer network security problems. There are nonlinear relations among the evaluation indexes, and it is difficult for an accurate mathematical model to describe the nonlinear relationship. In order to improve the evaluation accuracy of computer network security, we put forward a combination model to evaluate the computer network security. The combination model used particle swarm optimization (PSO) to optimize the parameters of BP neural network, speed up the BP neural network's convergence speed, and enhance its global optimization ability, which effectively improved the accuracy of the evaluation model. Simulation results showed that compared with traditional BP neural network model, the combined model, learning ability is faster and global search ability is stronger, which effectively improves the evaluation accuracy of computer network security.

Copyright

© 2018, 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  - Jimin Gao
PY  - 2018/01
DA  - 2018/01
TI  - Research on Computer Network Security Evaluation Based on Neural Network
BT  - Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)
PB  - Atlantis Press
SP  - 665
EP  - 670
SN  - 2352-5401
UR  - https://doi.org/10.2991/macmc-17.2018.125
DO  - 10.2991/macmc-17.2018.125
ID  - Gao2018/01
ER  -