Evolutionary game decision-making method for network attack and defense based on regret minimization algorithm

In view of the differences and limitations of the cognitive abilities of both sides of network security attack and defense, the current network defense decision-making methods using game theory are mostly based on the assumption that the participants of attack and defense are completely rational, which is difficult to apply to the actual network attack and defense scenarios, resulting in poor practicality of defense decision-making. In order to better fit with the scenario of imperfect rational attack and defense games, we apply evolutionary game theory to describe the evolution process of attack and defense games under imperfect rationality, and extend the static analysis in the traditional game into a dynamic evolution process. Regret minimization (RM) algorithm is used to optimize the strategy learning mechanism to ensure the randomness and convergence of the strategy learning. Therefore, an evolutionary game decision model of network attack and defense based on RM algorithm is constructed. The optimal defense decision method is given by solving the evolutionary stable equilibrium, and the evolutionary trajectory of the optimal strategy of both sides is described. The results of numerical experiments verify the science and effectiveness of the model and method, and analyze and summarize the evolution rules of different attack and defense strategies under different states. Meanwhile, compared with the network defense decision method based on traditional replication dynamics, the convergence rate of the optimal defense strategy is improved by 12.8%, which proves the superiority of the proposed method in learning rate.