Abstract: The data in network information security is generally characterized by high dimension and complex scale. Network intrusion detection requires reasonable analysis of network intrusion information to screen out dangerous aggressive behaviors. With the continuous increase of data dimension, traditional distance-based clustering analysis method is no longer applicable. Therefore, it is more and more important to find an effective method to solve high-dimensional data clustering analysis. A high-dimensional data clustering analysis based on the Krylov subspace methods is proposed, which firstly projects high-dimensional data to lower dimensions, implements the data dimension reduction, and then reoccupies genetic K-means algorithm in low dimensional space for data clustering. The method can not only avoid the loss of data attributes, but also improve the efficiency of high-dimensional data clustering analysis. Finally, experiment on KDD Cup 99 verifies the effectiveness and accuracy of the method.

Key words: Krylov subspace methods, high-dimensional clustering, intrusion detection system, genetic algorithm, K-means algorithm, information security