基于机器学习的网络攻防实验设计与实现

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关键词：实验教学；网络安全；机器学习；攻击检测；特征选择；数据预处理；DDoS；人才培养中图分类号：TN711-34；TP393 文献标识码：A 文章编号：1004-373X（2026）07-0088-08

Designand implementation of network attack and defense experiment based on machine learning

CAOTengfei，ZHANG Hongrun，LIUWei （CollegeofComputerTechnologyandApplications，QinghaiUniversity，Xining81oo16，China）

Abstract：Inviewofthenetworksecurityissueandtheshortageofartificialinteligencetalentinthewesternregions，this paperdesignsandimplementsanetwork atackanddefenseexperimentalteaching program thatintegratesmachinelearning technology.Thisprogramaimstoenhancestudents’abilitytodesignandoperateinteligentnetworkatackanddefense.The programstarts withanApache DDoSatack detectionexperiment，where six machine learningalgorithms，includingK-nearest neighbor（KNN），supportvectormachine（SVM）andrandomforest （RF），areusedtosystematicallyguidestudentstooverthe entireprocessfromdatapre-processingandfeatureselectiontomodeltraining，enabling themtomasterthenetworkatack detection techniquesbasedonmachinelearning.Inaddition，byanetworkflowfeatureselectionexperiment，studentswilear howtooptimizemachinelearningalgorithmstoimprovetheaccuracyandeficiencyofnetworkatackdetection，thereby enhancingtheirabilitytoaddresscomplexnetworksecuritythreats.Inthisteachingexperiment，amachinelearning environment isdeployedonanetworkattackanddefenseexperimentplatform.Theteachingexperimentcanfosterstudents’inteligent networkatackanddefensethinking，andefectivelyenhancestudents’abilitytohandlecybersecurityissesTherefore，the experiment design provides essential support for talent development in the western universities.

Keywords：experiment teaching; network security;machine learning;attack detection;featureselection;data pre-procesing; DDoS;talent cultivation

0 引言

随着Web应用及组件漏洞的不断增加以及攻击方式的演进，安全漏洞层出不穷，攻击的方式也越来越多样化。（剩余8238字）