An Attack Threat Effect Analysis Method Based on K-Means Evaluation

To take full advantage of the specified features of the attack dataset in network attack effect evaluation, maximize the efficiency of evaluation without losing its accuracy. This paper proposed a K-Means evaluation technique using dimensional entropy components, derived from changes in network entropy through attack period and the advantages of clustering algorithm in data mining. This method makes a pre-process of the attack dataset on the basis of network entropy, mapping it to a two-dimensional plane and utilize the output of pre-process as the input of clustering. Then establish a relation between the attack dataset and the effect category via an improved K-Means algorithm, thus achieving an explicit division of attack effect set and provide efficient evaluation result. The experimental results prove that the method can process attack dataset with high efficiency, as well as provide a visualized evaluation result by the form of cluster tree.