网络安全态势多粒度分析的云方法

利用云理论的不确定性推理方法解决网络安全态势感知问题。在现有模型基础上，设计了一种基于知识库的多粒度网络安全态势感知模型。该模型具有动态更新知识库、多粒度态势展现以及态势可视化三大优势。另外，该模型实现了资产安全态势的扩展云推理算法，该算法能够处理连续值和离散值等多类参数，适用于多条件多规则推理，并且可以得到定性和定量两种安全态势结果。该模型具有较高的扩展性和实用性。最后，作者在武汉大学校园网中实验验证了模型的可行性及准确性。

Cloud method of multi-granularity network security situation analysis

This paper employs the uncertainty reasoning method in the Cloud Theory to solve the problem of network security perception. On the basis of the analysis of the existing models for security evaluation and their key technologies, the author proposed a situation awareness model using the knowledge base as well as the multi-granularity method. This model has three advantages over the conventional evaluation mechanisms: the dynamic updated knowledge base, multi-granularity method for security representation and visualization of the whole situation offers higher rate in accuracy and lower information redundancy. In addition, an extended cloud reasoning algorithm was designed and implemented to get both the qualitative and quantitative results of the monitored network and thus offers a more perceptible and scalable security situation. At last, experiments are done on the campus network of Wuhan University, and the results indicate that the feasibility and efficiency of our design.