基于机器学习的校园网络入侵检测方法研究  

Research on campus network intrusion detection method based on machine learning

作　　者：张弛 吕国[1] 孙皓月[1] 祝焦焦 ZHANG Chi;LV Guo;SUN Hao-yue;ZHU Jiao-jiao(Hebei University of Architecture,Zhangjiakou,Hebei 075000)

机构地区：[1]河北建筑工程学院,河北张家口075000

出　　处：《河北建筑工程学院学报》2022年第4期160-165,共6页Journal of Hebei Institute of Architecture and Civil Engineering

摘　　要：随着科学技术的发展,校园网络的安全越来越受到重视,在传统的校园网络安全中最常用的防火墙已经无法满足现在的需求,因此入侵检测应运而生,将入侵检测应用在校园网络安全中可以更好地应对来自内外部的攻击,但是入侵检测同样存在一些弊端,因此提出将lightGBM框架应用在入侵检测中,使入侵检测可以更好地应对各种攻击。With the development of science and technology,more and more attention has been paid to the security of campus network.The most commonly used firewall in traditional campus network security can no longer meet the current needs.Therefore,intrusion detection came into being.Applying intrusion detection to campus network security can better deal with internal and external attacks,but intrusion detection also has some disadvantages,Therefore,this paper proposes to apply the machine learning framework to intrusion detection,so that intrusion detection can better deal with various attacks.

关 键 词：机器学习 入侵检测 lightGBM 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]