Abstract: Feature selection and feature extraction are two kinds of dimensionality reduction techniques to boost classifiers' performance.Very little work on feature extraction is taken in the field of network anomaly detection.This paper applies principal component analysis(PCA) and kernel prncipal component analysis(KPCA) to network intrusion feature extraction.The extracted features are employed by SVM for classification.PCA linearly transforms the original inputs into new uncorrelated features while KPCA is an nonlinear generalization of the linear PCA using the kernal method.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 principal component regression(PCR) and kernel principal component regression(KPCR) classification methods and by SVM without(application) of feature extraction.The results clearly demonstrate that feature extraction can greatly reduce the dimensionality of feature space witout degrading the claaifiers' performance.Among these methods,the best performance is achieved by SVM using only the first four principal components extracted by(KPCA.)