Feature Extraction Method for Network Intrusion Detection Based on RS-KPCA

Fang Nian Wang; Shen Shen Wang; Yun Bai; Wan Fang Che

doi:10.4028/www.scientific.net/AMM.665.706

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 665Feature Extraction Method for Network Intrusion...

Feature Extraction Method for Network Intrusion Detection Based on RS-KPCA

Abstract:

For the complexity and nonlinearity of the input characteristics in network intrusion detection system, a feature extraction method for network intrusion detection based on RS-KPCA is studied. Firstly, the Rough Set (RS) theory is used to select the valuable features, while the unnecessary features are removed. Then, the features of the intrusion detection sample data are extracted by the kernel principal component analysis (KPCA) algorithm. The number of new features is determined by the cumulative contribution rate. Simulation results show that this method can effectively remove the interference features, and has the advantages of obvious principal component feature and concentrated contribution rate, compared with PCA. Overall, the proposed method can effectively integrate the nonlinear features of the original data, reduce the dimension, and improve the intrusion detection performance.

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Periodical:

Applied Mechanics and Materials (Volume 665)

Pages:

706-711

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.665.706

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Online since:

October 2014

Authors:

Fang Nian Wang, Shen Shen Wang, Yun Bai, Wan Fang Che

Keywords:

Feature Extraction, Intrusion Detection, Kernel Principal Component Analysis (KPCA), Rough Set (RS)

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References

[1] Yang Huihua, Wang Xingyu and Wang Yong. Control and Decision, Vol. 20(2005): p.251.

Google Scholar

[2] Gao Haihua, Yang Huihua and Wang Xingyu. Journal of East China University of Science and Technology (Natural Science Edition), Vol. 32 (2006): p.321.

Google Scholar

[3] Gao Haihua, Yang Huihua, and Wang Xingyu. Computer Engineering and Applications, Vol. 41(2005): p.145.

Google Scholar

[4] Hu Jinhai, Xie Shousheng and Wang Cheng. Journal of Aerospace Power, Vol. 23(2008): p.1346.

Google Scholar

[5] Guo Y F, Shu T T, Yang L Y. Pattern Analysis and Application, Vol. 4(2001): p.61.

Google Scholar

[6] V. N. Vapnik. Statistical Learning Theory, AT&T Research, London University(1998), p.112.

Google Scholar

[7] Shawe-Taylor J. Nello Cristianini. Kernel Method for Pattern Analysis, Cambridge University Press(2004), p.221.

Google Scholar

[8] David V, and Sanchez A. Neurocomputing, Vol. 55(2003): p.5.

Google Scholar