Kernel PCA based network intrusion feature extraction and detection using SVM

Authors:
Hai-Hua Gao;Hui-Hua Yang;Xing-Yu Wang
Affiliations:
School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China;School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China;School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

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Abstract

This paper proposes a novel intrusion detection approach by applying kernel principal component analysis (KPCA) for intrusion feature extraction and followed by support vector machine (SVM) for classification. The MIT's KDD Cup 99 dataset is used to evaluate these feature extraction methods, and classification performances achieved by SVM with PCA and KPCA feature extraction are compared with those obtained by PCR and KPCR classification methods and by SVM without application of feature extraction. The results clearly demonstrate that feature extraction can greatly reduce the dimension of input space without degrading the classifiers' performance. Among these methods, the best performance is achieved by SVM using only four principal components extracted by KPCA.