Abstract
In prediction of network security situation, the prediction accuracy of traditional single kernel function vector machine is a little low. It can’t describe the randomness and abruptness, and it has some limitation. A network security forecasting model was put forward which combined Gaussian kernel function and polynomial kernel to solve this problem. Proved by simulation experiment, this model can increase prediction accuracy and it has some practical meaning.
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Acknowledgments
This research work was supported by National High Technology Research and Development “Program 863” under Grant No.2013AA12A402. The scientific research and technology development plan of Guilin: “Four-wheel positioning mobile cloud platform”, No.Gui Ke Gong 20150103-9. Special project of information service development, Guangxi, 2015: “Huge amount of data analysis, processing and customer service of Vehicle chassis based on Hadoop”, Gui Gong Xin Dian Ruan, No.2015-239 and Special project of information service development, Guilin, 2014: “M4G four-wheel positioning mobile system”.
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Xie, X., Long, Z., Gu, F. (2016). Prediction on Internet Safety Situation of Relevance Vector Machine about GP-RVM Kernel Function. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_76
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DOI: https://doi.org/10.1007/978-981-10-0356-1_76
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