Attack prediction model based on dynamic bayesian games
基于动态贝叶斯博弈的攻击预测模型

This paper described an attack prediction model based on dynamic bayesian games. According to the historical behaviors of the attacker, this model reasonably updates the probability of malicious nodes existing in the network by using bayesian law, with which it can predict the probability of attacks or defenses that rationale attacker or defender will take in the next stage of the game, in order to maximize their payoff. Thus the result can be used to assist security administrators to configure the network system. It may change the passive detection to the active protection for the defender. This paper also presented the process of experiment and analysis result for validity of the model.