基于聚类分析的可信网络管理模型

针对可信网络中动态信任模型对终端用户行为信任评估有效性问题，提出一种新的基于聚类分析的可信网络管理模型。该模型在传统信任模型的基础上引入聚类分析方法，从行为预期的角度研究用户的行为信任。通过对用户的历史行为数据进行聚类分析以构建行为预期，并利用行为预期评估用户行为，最后以信任评估结果为依据实现对网络中的用户的管理。实验表明该模型可以对长期接入的正常用户产生稳定的信任评估结果，同时迅速发现并隔离恶意用户，对可信用户与不可信用户有较高的区分度，与传统的信任模型相比具有更高的准确度及效率，达到了提高网络可信性的目的。

Trusted network management model based on clustering analysis

To improve the availability of dynamic trust model in trusted network, a trusted network management model based on clustering analysis was built. Behavior expectations were used to describe the trust of user behavior by introducing clustering analysis to the traditional trust model. Clustering analysis of the user's historical data was used to build behavior expection model, which was used to evaluate user's behaviors. Finally the trust evaluation results were utilized to realize the network user management. The experimental results show that the proposed model can generate trust evaluation results firmly, detect and isolate the malicious users rapidly, it has better accuracy and efficiency than traditional model, basically improving the network reliability.