网格环境下一种基于分类的行为信任模型

在网格环境中，针对不同上下文环境下实体之间的交互行为，提出了一种新的基于分类的行为信任模型.模型根据信任等级对实体进行相应分类，在此基础上与实际社会相结合引入了交互事件重要性因子，惩罚因子，惩罚加速因子等，实现了实体动态的主体特征.分析和仿真表明，模型在性能上得到了一定程度的提高，并对恶意实体的恶意交互行为起到了一定的抑制作用.

A behavior trust model based on classification in grid environment

A new behavior trust model based on classification is proposed to deal with the entity interactive behavior of different context in grid environment. The model classifies the entities according to the entity trust rank defined by entity trust value, then paper introduces the interactive event importance factor, the penalty factor, the penalty acceleration factor and so on by associating the model with actual society, that realizes the autonomous characteristic of dynamic entity. The analysis and the simulation indicates that the model has a certain extent enhancement in the performance, and plays a certain inhibitory action to the malicious entity's malicious interactive behaviors.