Network Intrusion Detection Method Based on RS-LSSVM

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

doi:10.4028/www.scientific.net/AMM.602-605.1634

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605Network Intrusion Detection Method Based on...

Network Intrusion Detection Method Based on RS-LSSVM

Abstract:

An intrusion detection method based on RS-LSSVM is studied in this paper. Firstly, attribute reduction algorithm based on the generalized decision table is proposed to remove the interference features and reduce the dimension of input feature space. Then the classification method based on least square support vector machine (LSSVM) is analyzed. The sample data after dimension reduction is used for LSSVM training, and the LSSVM classification model is obtained, which forms the ability of detecting unknown intrusion. Simulation results show that the proposed method can effectively remove the unnecessary features and improve the performance of network intrusion detection.

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Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

1634-1637

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.1634

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Online since:

August 2014

Authors:

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

Keywords:

Attribute Reduction, Intrusion Detection, LSSVM, Rough Set (RS)

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References

[1] 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

[2] Zhu Shouye. Computer Engineering and Applications, Vol. 45 (2009), p.123.

Google Scholar

[3] Dai Tianhong, Wang Keqi, Yang Shaochun. China Safety Science Journal, Vol. 18(2008), p.126.

Google Scholar

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

Google Scholar

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

Google Scholar

[6] Wu Di and Zhang Yaping. Journal of Computer Research and Development, Vol. 43(2006), p.437.

Google Scholar

[7] Zhang Wenxiu: Rough Set theory and method (Science Press, China 2001).

Google Scholar

[8] Li Lijuan, Su HongYe and Chu Jian. Acta Automatica Sinica, Vol. 33(2007), p.1182.

Google Scholar