In the multistep attack scenario, each rational attack-defense player tries to maximize his payoff, but the uncertainty about his adversary prevents him from taking the favorable actions. How to select the best strategy from the candidate strategies to maximize the defense payoff becomes the core issue. For this purpose, the paper innovatively designs a game theory model from the point of network survivability in combination with the attribute attack graph. The attack graph is created based on the network connectivity and known vulnerabilities using the MulVAL toolkit, which gives the full view of all the known vulnerabilities and their interdependence. Then, we use the attack graph to extract attack-defense actions, candidate attack-defense strategies, attack-defense payoffs, and network states, as well as other game modeling elements. Afterwards, the payoffs of attack-defense strategies are quantified by integrating attack-defense strength and network survivability. In addition, we input the above elements into the game model. Through repeated learning, deduction, and improvement, we can optimize the layout of defense strategies. Finally, the efficient strategy selection approach is designed on the tradeoff between defense cost and benefit. The simulation of attack-defense confrontation in small-scale LAN shows that the proposed approach is reliable and effective.
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