科技创新与应用2025，Vol.15Issue(18)：5-8,16,5.DOI:10.19981/j.CN23-1581/G3.2025.18.002
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
摘要
Abstract
In order to improve the security level of industrial control systems and reduce the threat caused by network attacks,a protection strategy selection algorithm combining Q-learning and particle swarm optimization algorithm is proposed.The experimental results show that,when no protective strategy is implemented,the benefits that can be obtained from the attack can reach up to 547.3.After implementing the particle swarm algorithm and Bayesian attack graph selection protection strategy,the benefits obtained from the attack decreased to 432.5 and 398.7,respectively.When implementing the protective strategy selected by the improved particle swarm optimization algorithm based on Q-learning,the attack benefit decreased to 325.6.The above results indicate that the protection strategy selected by the improved particle swarm optimization algorithm based on Q-learning can significantly reduce attack benefits and effectively protect industrial control systems from network attacks.关键词
工控系统/安全风险/Q学习/粒子群优化算法/防护策略Key words
industrial control system/security risk/Q learning/particle swarm optimization algorithm/protection strategy分类
计算机与自动化引用本文复制引用
王靖夫,秦卫丽..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[J].科技创新与应用,2025,15(18):5-8,16,5.基金项目
中国烟草总公司河南省公司2022年管理重大创新项目(HNYGLCX202324、20234110000240036) （HNYGLCX202324、20234110000240036）
