基于Q学习与粒子群优化算法的工控系统安全防护策略选择模型

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中图分类号：TP391 文献标志码：A 文章编号：2095-2945（2025）18-0005-05

Abstract：Inordertoimprove thesecuritylevelofindustrialcontrolsystemsandreducethethreatcausedbynetwork attacks，aprotectionstrategyselectionalgorithmcombiningQ-learingandparticleswarmoptimizationalgorithmisproposedThe experimentalresultsshowthat，whennoprotectivestrategyisimplemented，thebenefitsthatcanbebtainedfromtheattckcan reachupto547.3.AfterimplementingtheparticleswarmalgorithmandBayesianatackgraphselectionprotectionstrategy，the benefitsobtainedfromtheatackdecreasedto432.5and398.7respectivelyWhenimplementingtheprotectivestrategyselected bytheimprovedparticleswarmoptimizationalgorithmbasedonQ-learning，theatackbenefitdecreasedto325.6.Theabove resultsindicatethattheprotectionstrategyselectedbytheimprovedparticleswarmoptimizationalgorithmbasedonQ-learning cansignificantlyreduceattack benefitsand effectivelyprotect industrial control systems from network atacks.

Keywords： industrial control system;securityrisk; Qleaming;particleswarmoptimizationalgorithm; protectionstrategy

随着工业控制系统网络化浪潮的推进，工控系统的电子化程度逐渐增加。（剩余6032字）