Evaluation Method of the Attack Effect of Network Based on Rough Set and KNN

Abstract

Rough set theory, based on its advantages of calculating index weights without relying on prior knowledge, is introduced into the methods of the attack effect of network. However, classical rough set theory has some shortcomings that can't handle incomplete datasets, and the lack of data will affect the accuracy of evaluation. Usually, the method of deleting the missing parts is adopted in the relevant assessment process, which is easy to cause data waste. This paper presents a method of the attack effect of network based on the combination of rough set theory and KNN interpolation algorithm. Through the KNN interpolation algorithm, the incomplete dataset is complemented, so that the processing error of the rough set can be reduced as much as possible. The experimental results show that the proposed evaluation method has better accuracy and objectivity.