基于因果知识网络的攻击场景构建方法

Attack Scenario Construction Method Based on Causal Knowledge Net

针对现有因告警缺失及冗余造成的攻击场景构建不准确的问题,提出了基于因果知识网络的攻击场景构建方法.首先依据专家知识定义因果关系,利用真实告警数据挖掘出能够定量刻画因果关系的因果知识,并对其进行显著性检验,以保证因果关系与因果知识的一致性和准确度,进而构成因果知识网络;然后借助因果知识网络,将攻击场景的构建分为初建与重构2步:1)通过告警映射与聚类定性得到初步的攻击场景;2)利用最大后验估计原理对其进行定量推理重构,得到完整的攻击场景.实验结果表明:该方法能利用专家知识和数据挖掘相结合的优势能够提高攻击场景构建的准确度.

In view of the problem that the existing attack scenario construction methods are not accurate due to the lack of consideration of alarm missing and alarm redundancy,a new attack scenario construction method based on causal knowledge net is put forward.The causal knowledge net is composed of causal relationship and causal knowledge.Firstly,the causal relationship of single-step attacks is defined according to the expert knowledge,and then the real alarms are utilized to mine the causal knowledge,which can be used to quantitatively describe the causal relationship.In particular,the significance testing mean is designed to guarantee the consistency and accuracy of the causal relationship as well as causal knowledge among the mining causal knowledge.Additionally,the attack scenario construction method can be divided into two different steps with the help of causal knowledge net:the initiatory attack scenario can be obtained by means of alarm mapping and clustering in the first step,and in the second step,the initiatory attack scenario is reconstructed and the intact attack scenario is achieved by taking advantage of the theory named maximum a posteriori estimation.Experimental results show that the proposed method can improve the accuracy of attack scenario construction by combining the advantages of expert knowledge and data mining.