Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

Abstract EN

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.