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A reconfigurable architecture for network intrusion detection using principal component analysis
2006
Proceedings of the internation symposium on Field programmable gate arrays - FPGA'06
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 vectors
doi:10.1145/1117201.1117262
dblp:conf/fpga/NguyenMC06
fatcat:icou4tbqlvhffpdyiqwohm7mqm