网络安全态势感知技术对于评估网络安全状况及预测攻击行为路径有着重要的意义。传统的网络态势评估方法大多偏重于在理论层面的静态分析，难以应用到实际网络环境中；且传感器收集到的数据庞大繁杂，易造成存储空间负载过大。针对上述问题，本文结合博弈论算法与强化学习算法，提出了一种结合博弈论与强化学习的网络攻防动态感知模型以分析网络态势安全及预测攻击路径。首先，设计带有优先级关系矩阵的层次分析法计算系统损失及安全态势；其次，引入Boltzmann概率分布法计算混合策略纳什均衡；最后改进Q-Learning与博弈论算法对网络状态转移进行动态分析，达到准确预测攻击路径、选择最优防御策略的目的。通过网络仿真实验，验证该模型具有有效性和可行性。

Traditional network situation assessment methods mostly focus on theoretical analysis, it is difficult to apply to the actual network environment; the data collected by sensors is complex, which easily causes excessive storage space load. In response to the above problems, combining reinforcement learning and game theory algorithms, proposing a network attack-defense dynamic perception model based on reinforcement learning and game theory to analyze network situation security and predict attack paths. First, designing the hierarchy analytic process with priority relation matrix to calculate system losses and the security situation; Secondly, using the Boltzmann probability distribution method to calculated the mixed strategy Nash equilibrium to find the best strategy. Finally, improving the Q-learning method and game theory to dynamic analyze the network state transition, to accurately predict the attack path and assist the defender choose the best defense through network simulation experiments, it is verified that the model is effective and feasible.

TP393

国家电网有限公司总部科技项目：面向电力物联网端到端安全防护体系关键技术研究及应用资助(520626190067)