The limitations of the professional knowledge and cognitive capabilities of both attackers and defenders mean that moving target attack-defense conflicts are not completely rational, which makes it difficult to select optimal moving target defense strategies difficult for use in real-world attack-defense scenarios. Starting from the imperfect rationality of both attack-defense, we construct a Wright-Fisher process-based moving target defense strategy evolution model called WF-MTD. In our method, we introduce rationality parameters to describe the strategy learning capabilities of both the attacker and the defender. By solving for the evolutionarily stable equilibrium, we develop a method for selecting the optimal defense strategy for moving targets and describe the evolution trajectories of the attack-defense strategies. Our experimental results in our example of a typical network information system show that WF-MTD selects appropriate MTD strategies in different states along different attack paths, with good effectiveness and broad applicability. In addition, compared with no hopping strategy, fixed periodic route hopping strategy, and random periodic route hopping strategy, the route hopping strategy based on WF-MTD increase defense payoffs by 58.7%, 27.6%, and 24.6%, respectively.

In the context of electric power grid cybersecurity, latency can significantly impact the stable operation, real-time data transmission, and security of the grid. Optimizing security strategies can effectively mitigate the impact of latency on the cybersecurity of the power grid. Particularly in the context of power grids, facing sophisticated and advanced APT (Advanced Persistent Threat) attacks, formulating scientific and rational security strategies is crucial for reducing the time delays incurred during the execution of these strategies. In this paper, we propose a security strategy decision-making method for the deployment of PLC Honeypoints in the Power Internet of Things (PIoT), based on the time-delayed evolution game to select the optimal defense strategy. Firstly, we establish a time-delayed evolutionary game model for the attack and defense of PLC Honeypoints, derive the time-delay differential equations for attack and defense strategies, and obtain the rules for updating the attack and defense strategies. Secondly, we implement a decision-making algorithm for PLC Honeypoint security defense strategies to solve for the optimal PLC Honeypoint defense strategy. Finally, the simulation results show that our model had considerable advantages over traditional replication dynamics. Although it affected the convergence rate of the strategy to reach the steady state, the time delay could ensure the stability of the optimal strategy result. In addition, as the impact of PLC Honeypoint defense on attack latency increased, the time delay in attack strategy-making increased, which can make timely adjustments and reduce losses to the defense in a timely manner. We use the percentage of load loss of the target power grid to construct attack-defense payoff. The attack-defense test in IEEE 22-Bus data set shows that the proposed scheme can effectively select the optimal PLC honeypoint strategy in 35th time.