Abstract
In order to improve the classification effectiveness of SVM (Support Vector Machine), new model is provided to optimize and improve it. Penalty parameter c and kernel function parameter g of SVM are optimized using genetic algorithm of binary coding, and the optimized model GA-SVM is established. The dimensionality of input sample for SVM is reduced by PCA (Principal Component Analysis) and the model GA-PCA-SVM is established. The famous KDD Cup 1999 dataset is used to evaluate the proposed model and experiment is carried out based on GA-SVM and GA-PCA-SVM. By comparison, the results show that the genetic algorithm optimization improves the classification accuracy rate of SVM and PCA operation shorts the training time and test time.
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Zhao, Jh., Li, Wh. (2012). Improvement Intrusion Detection Based on SVM. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34041-3_9
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DOI: https://doi.org/10.1007/978-3-642-34041-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34040-6
Online ISBN: 978-3-642-34041-3
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