基于行为监控的自适应动态信任度测模型

Adaptive Dynamic Trust Measurement and Prediction Model Based on Behavior Monitoring

大规模分布式系统中的动态信任关系模型本质上是最复杂的社会关系之一,涉及假设、期望、行为和环境等多种因子,很难准确的定量表示和预测.将粗糙集理论和信息熵理论结合起来,应用于开放环境下动态构建基于行为数据监控与分析的信任关系度测(度量与预测)模型.该方法直接从分析传感器监测到的动态数据入手,针对影响信任的多个度测指标进行自适应的数据挖掘与知识发现,从而改变了传统的信任关系建模思路,跳出了传统信任关系建模过程中各种主观假设的束缚,并克服了传统模型对多维数据处理能力不足的问题.实验结果表明,与已有模型相比,新模型能够快速准确地实现开放分布式环境下实体的可信性判别,而且具有良好的行为数据规模的扩展能力.

In the large-scale distributed systems, trust relationship model is one of the most complex concepts in social relationships, and it also is an abstract psychological cognitive process, involving assumptions, expectations, behavior and the environment, and other factors. So, it is very difficult to quantify and predict trust relationship accurately. In this paper, rough set theory and information entropy theory are combined and applied to the study of distributed dynamic trust measurement and prediction model based on behavior data. The new model works through analysis monitored behavior data by sensors directly, changes the traditional modeling thoughts, brakes away from the fetter of various subjective assumptions in traditional modeling methods, and overcomes the problem of inadequate handling capacity for multi-source behavior data in the traditional trust model. Simulating results shows that the new model can accurately implement trust measurement and prediction process between entities in open and complex distributed environment, and has a better scalable capacity of behavior data.