Most current intrusion detection methods cannot process large amounts of audit data for real-time operation. In this paper, anomaly network intrusion detection method based on Principle Component Analysis (PCA) for data reduction and classifier in presented. Each network connection is transformed into an input data vector. Moreover, PCA is applied to reduce the high dimensional data vectors and distance between a vector, and its projection onto the subspace. Based on the preliminary analysis using a set of benchmark data from KDD (Knowledge Discovery and Data Mining) Competition designed by DARPA, PCA demonstrates the ability to reduce huge dimensional data into a lower dimensional subspace without losing important information. This finding can be used to further enhance the detection accuracy in detecting new types of intrusion by taking PCA as the preprocessing requirement in reducing high dimensional data
