基于Q學習與粒子群優(yōu)化算法的工控系統(tǒng)安全防護策略選擇模型

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中圖分類號：TP391 文獻標志碼：A 文章編號：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

隨著工業(yè)控制系統(tǒng)網(wǎng)絡化浪潮的推進，工控系統(tǒng)的電子化程度逐漸增加。（剩余6032字）