Prediction Method of Network Security Situation Based on GA-LSSVM Time Series Analysis

Prediction Method of Network Security Situation Based on GA-LSSVM Time Series Analysis

Huan Wang Jian Gu Jianping ZhaoDan Liu Xin Sui Xiaoqiang Di Bo Li

School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China

School of Opto-electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China

Corresponding Author Email: 
471745553@qq.com
Page: 
370-388
|
DOI: 
https://doi.org/10.18280/ama_b.600208
Received: 
15 May 2017
|
Accepted: 
10 June 2017
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

To more accurately understand the development trend of network security situation and to solve the prediction problem in network security situation awareness, this paper proposes a prediction model and an optimization method of network security situation based on GA-LSSVM time series analysis. The model adopts the original sequence accumulation method to reduce the interference of the irregular fluctuations of the original sequence and constructs the mixed kernel function based on the combination of RBF and Poly which takes both the learning and generalization ability of the model into account. The genetic algorithm is used to optimize the parameters of the LSSVM model. Through characteristic chromosome coding of the model parameters, the search space is established to obtain the optimal solution through fitness evaluation. The simulation results show that the model can effectively predict the network security situation with an accuracy of about 13% higher than that of HHGA-RBFNN and PSO-SVM.

Keywords: 

LSSVM, Trend prediction, Parameter optimization

1. Introduction
2. The LSSVM-based Prediction Model
3. GA-based LSSVM Joint Parameter Optimization
4. GA-LSSVM-based Prediction
5. Experimental Simulation
6. Conclusion
Acknowledgements
  References

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