动态信任预测的认知模型  

Cognitive Model of Dynamic Trust Forecasting

作　　者：李小勇[1] 桂小林[2] 

机构地区：[1]智能通信软件与多媒体北京市重点实验室(北京邮电大学),北京100876 [2]西安交通大学电子与信息工程学院,陕西西安710049

出　　处：《软件学报》2010年第1期163-176,共14页Journal of Software

基　　金：国家自然科学基金No.60873071;国家高技术研究发展计划863No.2008AA01Z410; 中央高校基本科研业务资助No.BUPT 2009RC0201~~

摘　　要：开放系统中的信任关系本质上是最复杂的社会关系之一,涉及到假设、期望、行为和环境等多种因子,很难准确地定量表示和预测.结合人类社会的认知行为,提出了一种符合人类心理认知习惯的动态信任预测模型:(1)构建了自适应的基于历史证据窗口的总体可信性决策方法,不但克服了已有模型常用的确定权重的主观判断方法,而且可以解决直接证据不足时的可信性预测问题;(2)使用已有的DTT(direct trust tree)机制进行全局反馈信任信息的搜索与聚合,以降低网络带宽消耗,增强系统在大规模分布式系统中的可扩展性;(3)引入诱导有序加权平均(induced ordered weighted averaging,简称IOWA)算子的概念,建立了基于IOWA算子的直接信任预测模型,可以用来解决传统预测模型动态适应能力不足的问题.实验结果表明,与已有模型相比,该模型具有更稳健的动态适应性,在模型的预测准确性方面也有显著的改善.In an open system, trust is one of the most complex concept in social relationships, involving many decision factors, such as assumptions, expectations and behaviors, etc. So, it is very difficult to quantify and forecast accurately. Combined with human social cognitive behaviors, a new dynamic trust forecasting model is proposed. Firstly, a new and adaptive trusted decision-making method based on historical evidences window is proposed, which can not only reduce the risk and improve system efficiency, but also solve trust measurement and forecasting problem when the direct evidences are insufficient. Then, a feedback trust information aggregating algorithm is used based on DTT （direct trust tree）. Finally, Induced Ordered Weighted Averaging （IOWA） operator is introduced to construct the new combined direct dynamic trust forecasting model, to make up the shortage of traditional method, and the model hence can have a better rationality and a higher practicability. Simulations＇ computing results show that compared with the existing trust forecasting metrics, the model in this paper is more robust on trust dynamic adaptability, and has more remarkable enhancements in the forecasting accuracy.

关 键 词：分布式系统 信息安全 动态信任预测模型 诱导有序加权平均算子 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]