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A new approach for intrusion detection based on Local Linear Embedding algorithm

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2 Author(s)
Ying-Hui Kong ; Dept. of Electron. & Commun. Eng., North China Electr. Power Univ., Baoding, China ; Hai-Ming Xiao

Intrusion detection is a important network security research direction. SVM (support vector machine) is considered as a good substitute for traditional learning classification approach, and has a good generalization performance especially in small samples in non-linear case. LLE (local linear embedding) is a good nonlinear dimensionality reduction method, which is good for the data that lies on the nonlinear manifold. This paper proposes an approach using SVM and LLE in intrusion detection system. In the Matlab simulation experiment, we can achieve higher classification accuracy rate, lower false positive rare and false negative rate using the method, compared to PCA (principal component analysis) and ICA (independent component analysis) approach.

Published in:

Wavelet Analysis and Pattern Recognition, 2009. ICWAPR 2009. International Conference on

Date of Conference:

12-15 July 2009