Journal ArticleDOI
The Application of an Improved Integration Algorithm of Support Vector Machine to the Prediction of Network Security Situation
TLDR
Through the experiment on MATLAB for network security situational prediction, the results show that the absolute prediction error is smaller, the right trend rate is higher, and the algorithm chooses the high weights of SVM to integrate.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.read more
Citations
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Algorithm of Support Vector Machine Ensemble Based on Negative Correlation Learning
TL;DR: Simulation shows that this method can not only solve model selection problem of SVM, but also improve SVM generalization performance effectively with small cost.
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A Network Security Situation Awareness Method Based on GRU in Big Data Environment
TL;DR: In this article , a gate recurrent unit (GRU) model is established to effectively extract features from the situation data set through the deep learning algorithm of big data, which can effectively perceive the network threat situation without relying on data labels, which verifies that this method can effectively improve the efficiency and accuracy of security situation awareness.
References
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Journal Article
RBFNN-based Prediction of Networks Security Situation
TL;DR: Experiment results show that this method based on RBF neural networks can achieve perfect prediction, helping administrator to make a proper decision to protect the network.
Journal Article
Research on Network Attack Situation Forecast Technique Based on Support Vector Machine
TL;DR: The method of support vector regression forecast is used to forecast the indicator of network attack situation evaluation in time series and the framework of learning module and forecast module in the algorithm are produced.
Journal Article
GeesePSO:An Efficient Improvement to Particle Swarm Optimization
TL;DR: An improved algorithm is proposed using the characteristics of the flight of geese for reference and the experimental results show that the new algorithm not only significantly speed up the convergence, but also effectively solve the premature convergence problem.
Journal Article
Algorithm of Support Vector Machine Ensemble Based on Negative Correlation Learning
TL;DR: Simulation shows that this method can not only solve model selection problem of SVM, but also improve SVM generalization performance effectively with small cost.