A PCA-LVQ Model for Intrusion Alert Analysis

Abstract

We present a PCA-LVQ model and a balanced-training method for efficient intrusion alert analysis. For the connection records in the 1999 DARPA intrusion dataset, we firstly get a dimension-reduced dataset through Principal Component Analysis (PCA). Then, we use the Learning Vector Quantization (LVQ) neural network to perform intrusion alert clustering on the purified intrusion dataset. The experiment results show that the PCA-LVQ model and the balanced-training method are effective: the time costs can be shortened about by three times, and the accuracy of detection can be elevated to a higher level, especially for the U2R and R2L alerts.