The basic hypothesis of evolutionary game theory is that the players in the game possess limited rationality. The interactive behavior of players can be described by a learning mechanism that has theoretical advantages in modeling the network security problem in a real society. The current network security evolutionary game model generally adopts a replicator dynamic learning mechanism and assumes that the interaction between players in the group conforms to the characteristics of uniform mixed distribution. However, in an actual network attack and defense scenario, the players in the game have limited learning capability and can only interact with others within a limited range. To address this, we improved the learning mechanism based on the network topology, established the learning object set based on the learning range of the players, used the Fermi function to calculate the transition probability to the learning object strategy, and employed random noise to describe the degree of irrational influence in the learning process. On this basis, we built an attack and defense evolutionary network game model, analyzed the evolutionary process of attack and defense strategy, solved the evolution equilibrium, and designed a defense strategy selection algorithm. The effectiveness of the model and method is verified by conducting simulation experiments for the transition probability of the players and the evolutionary process of the defense group strategy.

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基于进化网络博弈的最优网络防御策略选择方法

进化博弈论的基本假设是，博弈中的参与者具有有限的理性。玩家的互动行为可以通过一种学习机制来描述，该机制在模拟现实社会中的网络安全问题时具有理论上的优势。当前的网络安全进化游戏模型通常采用复制者动态学习机制，并假设组内玩家之间的交互符合均匀混合分布的特征。但是，在实际的网络攻防场景中，游戏中的玩家学习能力有限，并且只能与有限范围内的其他人互动。为了解决这个问题，我们改进了基于网络拓扑的学习机制，根据玩家的学习范围建立了学习对象集，使用费米（Fermi）函数计算向学习对象策略的过渡概率，并采用随机噪声描述学习过程中非理性影响的程度。在此基础上，建立了攻防进化网络博弈模型，分析了攻防策略的演化过程，解决了进化均衡问题，设计了防御策略选择算法。该模型和方法的有效性通过进行模拟实验来验证球员的转移概率和防御小组策略的演化过程。建立了攻防进化网络博弈模型，分析了攻防策略的演化过程，解决了进化均衡问题，设计了防御策略选择算法。该模型和方法的有效性通过进行模拟实验来验证球员的转移概率和防御小组策略的演化过程。建立了攻防进化网络博弈模型，分析了攻防策略的演化过程，解决了进化均衡问题，设计了防御策略选择算法。该模型和方法的有效性通过进行模拟实验来验证球员的转移概率和防御小组策略的演化过程。