基于二维熵分量的K均值攻击效果评估

A K-Means Cluster Evaluation of Attack Effect Based on Bi-Dimensional Entropy Components

提出了一种利用二维熵分量的K均值攻击效果聚类评估方法.利用网络熵预处理攻击数据集,将效果数据映射到二维平面,并以二维熵分量作为聚类的输入,然后基于K均值算法建立聚类数据集与效果分类之间的关系,实现了对网络攻击效果结果集的明确划分,并提供快速有效的评估结果.仿真实验结果证明,该方法能高效正确地处理攻击数据,并以评估结果类图的形式提供直观的评估结果.

A K-means cluster evaluation technique using bi-dimensional entropy components was pro- posed. The attack dataset on the basis of network entropy was preproeessed, a two-dimensional plane was mapped. The output of preprocess as the input of clustering was utilized. And a relation between the at- tack dataset and the effect category on the basis of K-means algorithm was established, thus an explicit division of attack effect set was achieved. Efficient evaluation was given. Experiment shows that the method can process attack dataset with high efficiency, as well as provide a visualized evaluation result by form of evaluation cluster diagram.