Network Situation Awareness Model Prediction Method Based on Genetic Optimization Support Vector Machine

DOI

10.2991/cnct-16.2017.69How to use a DOI?

Keywords

Support vector machine, Genetic algorithm optimization, Network situation awareness, Model, Prediction

Abstract

The support vector machine model is based on the network security situation has strong randomness, is affected by many factors, and the number of types of network security incidents is uncertain, the reference sample is small, the prediction model of need "intelligent", according to SVM forecast algorithm. In order to select the parameters of SVM, genetic algorithm is introduced into the parameter selection in support vector machine, genetic algorithm optimization based on support vector machine structure (GA - SVM) situation awareness prediction model and method to measure data dimensionality reduction using principal component analysis, through simulation experiments, it is proved that this model is the prediction higher precision than neural network, classification and regression tree and cluster analysis prediction model.

Copyright

© 2017, 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  - Wei-peng CHEN
AU  - Zhi-gang AO
AU  - Yi-qiang TU
AU  - Xing-dang KANG
AU  - Zhen-nan ZHAO
PY  - 2016/12
DA  - 2016/12
TI  - Network Situation Awareness Model Prediction Method Based on Genetic Optimization Support Vector Machine
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 493
EP  - 500
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.69
DO  - 10.2991/cnct-16.2017.69
ID  - CHEN2016/12
ER  -