基于不完全信息随机博弈的防御决策方法

现有防御决策中的随机博弈模型大多由矩阵博弈与马尔可夫决策组成，矩阵博弈中假定防御者已知攻击者收益，与实际不符。将攻击者收益的不确定性转换成对攻击者类型的不确定性，构建了由静态贝叶斯博弈与马尔可夫决策结合的不完全信息随机博弈模型，给出了不完全信息随机博弈模型的均衡求解方法，使用稳定贝叶斯纳什均衡指导防御者的策略选取。最后通过一个具体实例验证了模型的可行性和有效性。

Defense decision-making method based on incomplete information stochastic game

Most of the stochastic game models to select network defense strategies are composed of matrix game and Markov decision,which assumes that the defender has known the attacker's revenue.This assumption does not conform to the actual situation.The uncertainty of the attacker's income was converted into the indeterminacy of the attacker type,and an incomplete information stochastic game model,which was combined with the static Bias game and Markov decision,was constructed.The equilibrium solution method of the incomplete information stochastic game model was given,and the strategy selection of the defender was guided by the stable Bias Nash equilibrium.Finally,a practical example was given to demonstrate the feasibility and effectiveness of the model.