一种基于半监督学习算法的网络攻击检测系统  

A Network Attack Detection System Based on Semi-supervised Learning Algorithm

作　　者：张雅茹 ZHANG Ya-ru(School of Continuing Education,Lianyungang Open University,Lianyungang 222006,China)

机构地区：[1]连云港开放大学继续教育学院,江苏连云港222006

出　　处：《辽东学院学报（自然科学版）》2024年第1期47-53,共7页Journal of Eastern Liaoning University:Natural Science Edition

摘　　要：为应对网络攻击事件日益高发等难题,以自适应增强算法为基础,采用自我训练方式设计一种基于半监督学习的网络攻击检测算法。基于所提算法设计网络攻击检测系统,该设计系统主要包含数据采集、处理及检测单元。实验结果表明,在KDDTest数据集上,所提算法的准确率、精确率及召回率3项指标均高于半监督STBooost算法。在系统检测性能测试中,攻击流量的检测准确率均高于94.0%,满足网络攻击检测系统对设计准确率的要求。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.

关 键 词：半监督学习 网络安全 检测系统 自适应增强 攻击流量 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]