An Intrusion Detection Research Based on Spectral Clustering

A spectral clustering intrusion detection approach is presented in this paper. The basic idea of the approach is to compute the similarities between the training data points, then to construct the affinity matrix, and to get the clusters according the main eigenvector of this affinity matrix. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio. The benefits of the approach lie in that it is accurate in clustering and it needn ´t labeled training data sets. Using the data sets of KDD99, the experiment result shows that this approach can detect intrusions efficiently in the real network connections.