Markov Evolutionary Games for Network Defense Strategy Selection

• Main Authors: Jianming Huang, Hengwei Zhang, Jindong Wang
• Format: Article
• Language: English
• Published: IEEE 2017-01-01
• Series: IEEE Access
• Subjects: Network attack and defense; bounded rationality; evolutionary games; Markov decision-making process; multi-stage game equilibrium
• Online Access: https://ieeexplore.ieee.org/document/8039250/

Since the characteristics of opposite objectives, non-cooperation relationship, and dependent strategies of network attack and defense are highly consistent with game theory, researching the decision-making methods of network defense and applying the game models to analyze the network attack-defense behaviors has been of concern in recent years. However, most of the research achievements regarding to the game models are based on the hypothesis that both the two sides' players are completely rational, which is hard to meet. Therefore, we combined the evolutionary game theory and Markov decisionmaking process to construct a multi-stage Markov evolutionary game model for network attack-defense analysis, in view of the bounded rationality constraint. The model, based on the non-cooperative evolutionary game theory, could accomplish dynamic analysis and deduction for the multi-stage and multi-state network attack-defense process. In addition, an objective function with discounted total payoffs was designed by analyzing payoff characteristics of the multi-stage evolutionary game, which is more consistent with the reality of network attack and defense. Besides, the solving method for multi-stage game equilibrium was proposed on the basis of calculating the single-stage evolutionary game equilibrium. In addition, an algorithm for optimal defense strategy of the multi-stage evolutionary games was given. Finally, the experiments showed the high effectiveness and validity of the model and method that has a guiding significance for the network attack and defense.