重庆大学学报2025，Vol.48Issue(6)：84-97,14.DOI:10.11835/j.issn.1000-582X.2025.06.008
结合博弈论与强化学习的态势感知与路径预测
Situational awareness and path prediction combining game theory and reinforcement learning
摘要
Abstract
Cybersecurity situational awareness technology plays a critical role in assessing network security status,predicting potential attack paths,and assisting administrators in implementing effective defenses.Traditional methods for network situation assessment mostly rely on theoretical analysis,limiting their practicality in real-world networks.Additionally,the complexity of sensor-collected data often results in excessive storage demands.To address these challenges,this paper proposes a dynamic network attack-defense perception model that integrates reinforcement learning and game theory to enhance situational awareness and predict potential attack paths.The approach begins with the design of a hierarchical analytic process using a priority relation matrix to calculate system losses and assess security posture.Next,the Boltzmann probability distribution is employed to calculate the mixed-strategy Nash equilibrium,identifying optimal strategic responses.Finally,an improved Q-learning algorithm,in combination with game-theoretic principles,is used to dynamically model network state transitions,enabling accurate prediction of attack paths and supporting defenders in selecting optimal defense strategies.Simulation results validate the model's effectiveness and practicality in complex network environments.关键词
强化学习/Q-learning/博弈论/态势感知/层次分析法/纳什均衡Key words
reinforcement learning/Q-learning/game model/situational awareness/analytic hierarchy process/Nash equilibrium分类
信息技术与安全科学引用本文复制引用
杨云,梁花,魏兴慎,李洋,刘俊..结合博弈论与强化学习的态势感知与路径预测[J].重庆大学学报,2025,48(6):84-97,14.基金项目
重庆市电力公司科技项目(520626190067).Supported by Science and Technology Projects of State Grid Chongqing Electric Power Company(520626190067). （520626190067）
