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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural network design. China Machine Press, Beijing (2002)
Ashton, J.P.: Intrusion Detection System for Water Storage Facilities. Journal American Water Works Association 103(10), 34–44 (2011)
Patcha, A., Park, J.-M.: Network anomaly detection with incomplete audit data. Computer Networks 51(13), 3935–3955 (2007)
Tagelsir, E.H., Mohamed, O.I.: Alert correlation in collaborative intelligent intrusion detection systems-A survey 11(7), 4349–4365
Yi, Y., Wu, J., Xu, W.: Incremental SVM based on reserved set for network intrusion detection. Expert Systems with Applications 38(6), 7698–7707 (2011)
Swarup, K.S., Corthis, P.B.: ANN approach assesses system security. Computer Applications in Power 15(3), 32–38 (2002)
Yang, X.J., Zheng, J.L.: Artificial Neural Network. Higher Education Press, Beijing (1992)
Shi, F., Wang, S.C., Yu, L., et al.: Matlab neural network 30 cases analysis. Beijing University of Aeronautics and Astronautics Press, Beijing (2010)
Liu, Y., Sun, D.H., Chen, Y., et al.: An Intrusion Detection Method Based on Principal Component Analysis and Decision Tree. Journal of Northeastern University 31(7), 933–937 (2010)
Index of / databases/kddcup99 (EB/OL), http://kdd.ics.uci.edu/databases/kddcup99
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)