基于动态贝叶斯博弈的攻击预测模型

提出了基于动态贝叶斯博弈的攻击预测模型。该模型能根据攻击者的历史行为，使用贝叶斯法则对网络中存在恶意主机节点的概率进行合理的修正，并以此为基础，通过分析攻击者和防御者双方的收益，预测出理性的攻击者和防御者在下一个博弈阶段会选择攻击和防御的概率。预测结果为网络安全管理员进行安全配置提供了有价值的参考依据，从而使被动的检测变为主动的有针对性的防御成为可能。最后介绍了相应的实验过程和结果分析，验证了模型的有效性。

Attack prediction model based on dynamic bayesian games

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.