静态贝叶斯博弈最优防御策略选取方法

针对现有基于博弈理论的网络防御策略选取方法没有考虑攻防双方策略选取能力差异对博弈过程的影响和以概率的形式给出最优防御策略导致可操作性不强的问题,提出了一种基于静态贝叶斯博弈的最优防御策略选取方法。该方法从不完全信息角度对网络攻防过程进行建模;通过收敛度对攻防双方策略选取能力的差异进行量化;在对混合策略贝叶斯纳什均衡分析的基础上,对防御效能进行量化;以防御效能为标准进行最优防御策略的选取。通过实例分析验证了所提方法的合理性和可行性。与传统基于博弈理论的策略选取方法相比,该方法更加紧贴网络实际,具有更强的可操作性。

Optimal defense strategy selection based on the static Bayesian game

Nowadays, the network optimal defense strategy selection based on the game theory has many disadvantages, such as ignoring the influence of strategy selection ability between attacker and defender, and choosing the defense strategy improperly in the form of probability. In order to solve the problem, an optimal defense strategy selection method based on the static Bayesian game is proposed. The network attack-defense process is modeled from the perspective of incomplete information. The difference of strategy selection ability between attacker and defender is quantified by the convergence degree. The defense effectiveness is quantified on the basis of mixed strategy Bayesian Nash equilibrium. The optimal defense strategy is selected that takes defense effectiveness as the criterion. The rationality and feasibility of the method proposed in this paper are verified via a representative example. Compared with the traditional strategy selection method based on the game theory, this method is more closely related to the actual network and has a greater operability.