Abstract
Existing approaches of cyber attack-defense analysis based on stochastic game adopts the assumption of complete rationality, but in the actual cyber attack-defense, it is difficult for both sides of attacker and defender to meet the high requirement of complete rationality. For this aim, the influence of bounded rationality on attack-defense stochastic game is analyzed. We construct a stochastic game model. Aiming at the problem of state explosion when the number of network nodes increases, we design the attack-defense graph to compress the state space and extract network states and defense strategies. On this basis, the intelligent learning algorithm WoLF-PHC is introduced to carry out strategy learning and improvement. Then, the defense decision-making algorithm with online learning ability is designed, which helps to select the optimal defense strategy with the maximum payoff from the candidate strategy set. The obtained strategy is superior to previous evolutionary equilibrium strategy because it does not rely on prior data. By introducing eligibility trace to improve WoLF-PHC, the learning speed is further improved and the defense timeliness is significantly promoted.
Highlights
With the continuous strengthening of social informatization, cyber attacks are becoming more frequent, causing tremendous losses to defenders [1]
Game theory and cyber attack-defense have a high degree of opposition, non-cooperative relationship, and strategic dependence [2]. e research and application of game theory in cyber security are rising day by day [3]. e analysis of attack-defense confrontation based on stochastic game has become a hotspot
The cyber security analysis based on stochastic game has achieved some results, but there are still some shortcomings and challenges [4,5,6,7]. e existing stochastic game of attack-defense is based on the assumption of complete rationality, through Nash equilibrium for attack prediction and defense guidance
Summary
With the continuous strengthening of social informatization, cyber attacks are becoming more frequent, causing tremendous losses to defenders [1]. To solve the above problems, this paper studies the defense decision-making approach based on stochastic game under the restriction of bounded rationality. Using the improved intelligent learning algorithm WoLF-PHC (Wolf Mountain Climbing Strategy) to analyze the stochastic game model, we design the defense decision-making algorithm. Compared with the existing bounded rationality games, the approach proposed in this paper reduces the exchange of information among game players and is more suitable for guiding individual defense decision making. (3) WoLF-PHC algorithm is improved based on eligibility trace [10], which speeds up the learning speed of defenders, reduces the dependence of the algorithm on data, and proves the effectiveness of the approach through experiments
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