Optimal Network Defense Strategy Selection Based on Markov Bayesian Game

Abstract

The existing defense strategy selection methods based on game theory basically select the optimal defense strategy in the form of mixed strategy. However, it is hard for network managers to understand and implement the defense strategy in this way. To address this problem, we constructed the incomplete information stochastic game model for the dynamic analysis to predict multi-stage attack-defense process by combining Bayesian game theory and the Markov decision-making method. In addition, the payoffs are quantified from the impact value of attack-defense actions. Based on previous statements, we designed an optimal defense strategy selection method. The optimal defense strategy is selected, which regards defense effectiveness as the criterion. The proposed method is feasibly verified via a representative experiment. Compared to the classical strategy selection methods based on the game theory, the proposed method can select the optimal strategy of the multi-stage attack-defense process in the form of pure strategy, which has been proved more operable than the compared ones.

Tripartite evolutionary game in the process of network attack and defense

“…Nevertheless it is difficult for network administrators to understand and implement defense strategies in this way. Lu et al [16] proposed a method that combines Bayesian game theory with Markov decision method to build a random game model with incomplete information. Compared with the classical strategy selection method based on game theory, this method can select the optimal strategy in the form of pure strategy and has strong operability.…”