Abstract:

The existing network defense decision-making methods are largely based on the assumption of complete rationality of both attacker and defender, as well as deterministic game models. However, these approaches struggle to align with real-world network attack-defense scenarios, leading to poor practicality. To better adapt to the bounded rationality of network attack-defense games, a network defense decision-making method based on Moran process and stochastic evolutionary game model was constructed. A selection intensity coefficient was introduced to analyze the preference of both attacker and defender for dominant strategy. By solving the dynamic evolutionary equilibrium equations of attack-defense strategy, an optimal defensive strategy decision-making algorithm was designed, and the evolution trajectory of strategy selection was characterized. The results of numerical simulation experiments verify the scientificity and effectiveness of the proposed method, and the evolution trajectories of attack and defense strategies under different network states are analyzed and discussed. Furthermore, compare to network defense decision-making methods based on Wright-Fisher and replicator dynamics, the average convergence speed of the optimal defense strategy proposed in this article improves by 23.1% and 17.4% respectively, indicating the superiority of this method in terms of convergence rate.

Key words: network defense, Moran process, stochastic evolutionary game, evolutionary equilibrium, optimal defense strategy