一种基于半监督学习算法的网络攻击检测系统.

In order to cope with the increasing incidence of network attack events, a network attack detection algorithm based on semi-supervised learning was designed through self-training based on adaptive enhancement algorithms. A network attack detection system was designed based on this algorithm, which mainly included data acquisition, processing and well as detection units. The experimental results show that on the KDDTest dataset, the proposed algorithm outperforms the semi-supervised STBoot algorithm in terms of accuracy, precision, and recall. Which meets the requirements of the design accuracy for the network attack detection system. [ABSTRACT FROM AUTHOR]