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In this paper, we develop an architecture for principal component analysis (PCA) to be used as an outlier detection method for high-speed network intrusion detection systems (NIDS). PCA is a common statistical method used in multivariate optimization problems in order to reduce the dimensionality of data while retaining a large fraction of the data characteristic. First, PCA is used to project the training set onto eigenspace vectors representing the mean of the data. These eigenspace vectorsdoi:10.1145/1117201.1117262 dblp:conf/fpga/NguyenMC06 fatcat:icou4tbqlvhffpdyiqwohm7mqm