The Application of an Improved Integration Algorithm of Support Vector Machine to the Prediction of Network Security Situation

Shao Min Zhang; Bing Xia Li; Bao Yi Wang

doi:10.4028/www.scientific.net/AMM.513-517.2285

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517The Application of an Improved Integration...

The Application of an Improved Integration Algorithm of Support Vector Machine to the Prediction of Network Security Situation

Abstract:

In order to grasp the security situation of the network accurately and provide effective information for managers of network.GeesePSOSEN-SVM algorithm is proposed in this paper. It can produce and train multiple independent SVM through Bootstrap method and increase the degree of difference among SVM based on learning theories of negative correlation to construct the fitness function.GeesePSO algorithm is used to calculate the optimal weights of SVM.The algorithm chooses the high weights of SVM to integrate. At last, through the experiment on MATLAB for network security situational prediction,the results show that the absolute prediction error is smaller ,and the right trend rate is higher.

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Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

2285-2288

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.2285

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Online since:

February 2014

Authors:

Shao Min Zhang*, Bing Xia Li, Bao Yi Wang

Keywords:

Prediction of Network Security Situation, Selective Integration, Support Vector Machine (SVM)

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* - Corresponding Author

References

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