Optimal Network Defense Strategy Selection Based on Incomplete Information Evolutionary Game

The issue of selecting the optimal defense strategy in the dynamic adversarial network is difficult. To solve this problem, we start from the realistic bounded rationality of both the attacker and the defender. First, we build the Bayesian attack-defense evolutionary game model combining with incomplete information game scenario. Specifically, we convert the uncertainty of the strategy payoffs of attackers and defenders to the uncertainty of their related types. Meanwhile, both the set of player types and the set of game strategies can be expanded to n in our model. Furthermore, we improve the replicator dynamics equation by adding the selecting intensity factor to depict the noise effect. This reflects the randomness of decision making for players due to their bounded learning capacities. In this way, the static analysis in the traditional game is extended as a dynamic process. On this basis, we summarize the evolutions of different player types with different strategies. Finally, by calculating the evolutionary stable equilibrium, we give the algorithm of selecting optimal defense strategy and depict the evolutionary track of this strategy selected by the defender with time going by. Our method provides decision support for the network proactive defense toward moderate security. Moreover, the dynamic analysis efficiency of defense decision making is improved, and the predicting ability of the defense situation is enhanced. Experimental results verify the scientificity and availability of the proposed model and method.