Abstract
Based on simple incremental SVM, we proposed an improved incremental SVM algorithm (ISVM), and combined it into a kernel function U-RBF and applied it into network intrusion detection. The simulation results show that the improved kernel function U-RBF has played some role in saving training time and test time. The ISVM has eased the oscillation phenomenon in the process of the learning to some extent, and the stability of ISVM is relatively good.
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References
Wang, X.D., Zheng, C.Y., Wu, C.M., Zhang, H.D.: New algorithm for SVM-based incremental learning. Computer Applications 10(26), 2440–2443 (2006)
Laskov, P., Gehl, C., Kruger, S., Muller, K.: Incremental support vector learning: Analysis, Implementation and application. Journal of Machine Learning Research 7, 1909–1936 (2006)
Shilton, A., Palamiswami, M., Ralph, D., Tsoi, A.: Incremental training of support vector machines. IEEE Transactions on Neural Networks 16, 114–131 (2005)
Cheng, S., Shih, F.: An improved incremental training algorithm for support vector machines using active query. Pattern Recognition 40, 964–971 (2007)
Liang, Z.Z., Li, Y.F.: Incremental support vector machine learning in the primal and applications. Neurocomputing (February 20, 2009)
Deng, N.Y., Tian, Y.J.: A new method of data mining – support vector machines. Science Press (2004)
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© 2013 Springer International Publishing Switzerland
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Zhang, H., Yi, Y., Wu, J. (2013). Network Intrusion Detection System Based on Incremental Support Vector Machine. In: Ali, M., Bosse, T., Hindriks, K., Hoogendoorn, M., Jonker, C., Treur, J. (eds) Contemporary Challenges and Solutions in Applied Artificial Intelligence. Studies in Computational Intelligence, vol 489. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00651-2_13
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DOI: https://doi.org/10.1007/978-3-319-00651-2_13
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00650-5
Online ISBN: 978-3-319-00651-2
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