基于双枝模糊逻辑攻击模型的分层推理算法

A delaminating reasoning algorithm based on Both-Branch Fuzzy Logic to attack model BBFPAN

针对以模糊Petri网为理论基础的网络攻击模型BBFPAN自学习能力差的缺点,本文首先提出一种新的适用于对攻击模型BBFPAN进行层次式划分的分层算法,为将神经网络理论引入攻击模型的研究奠定了基础。为了使攻击模型BBFPAN能够清晰地反映网络攻击过程中各节点的状态变化情况,发现网络系统的漏洞,本文首次将双枝模糊逻辑理论应用于攻击模型BBF-PAN的推理计算,提出了BBFPAN模型推理的基本规则,给出了一种BBFPAN分层推理算法,并通过实验验证了算法的正确性。

Attack model BBFPAN based on fuzzy Petri net has some drawback such as the lack of learning mechanism. A delaminating algorithm which can partition the BBFPAN into several levels is presented. This algorithm can be applied to the study about the BBFPAN using the neural network. In order to make fine description to the network attacks and finding the weakness of the network system, the basic inference rules about BBFPAN is put forward which originally applies Both - Branch Fuzzy Logic into the attack modeling. After this problem, based on the basic inference roles, a delaminating reasoning algorithm is prop- osed which can enrich the reasoning method of the attack model BBFPAN and the effectiveness of the algo- rithm is verified by experiment.