基于Krylov子空间方法的网络入侵数据聚类

网络信息安全中的数据具有维数高、规模复杂等特性。网络入侵检测需要对网络入侵信息进行合理的分析，筛选出危险的带有攻击性的行为。随着数据维数的不断升高，传统的基于距离的聚类分析方法不再适用。针对此，本文提出一种基于Krylov子空间方法的高维数据聚类分析算法，首先将高维数据投影到低维空间，实现数据的降维，再用基于遗传算法的K-means算法在低维空间中进行数据的聚类，避免了数据属性的丢失，同时也提高了高维数据聚类分析的效率。最后，使用KDD Cup 99数据进行实验，实验验证了方法的有效性和精确性。

Network Intrusion Data Clustering Algorithm Based on Krylov Subspace

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 With the continuous increase of data dimension, traditional distance-based clustering analysis method is no longer applicable.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.