A reconfigurable architecture for network intrusion detection using principal component analysis

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 are then used to predict malicious connections in a workload containing normal and attack behavior. Our simulations show that our architecture correctly classifies attacks with detection rates exceed

• Type: Conference
• Year: 2006
• Where: FPGA
• Authors: David T. Nguyen, Gokhan Memik, Alok N. Choudhary
• Keywords: Eigenspace Vectors, FPGA, FPGA 2006, Network Intrusion Detection, Outlier Detection Method