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计算机工程 ›› 2020, Vol. 46 ›› Issue (4): 129-134. doi: 10.19678/j.issn.1000-3428.0054193

• 网络空间安全 • 上一篇    下一篇

志愿计算中基于贝叶斯定理的信任模型

徐玲1,2, 乔建忠1, 林树宽1, 祁瑞华2   

  1. 1. 东北大学 计算机科学与工程学院, 沈阳 110169;
    2. 大连外国语大学 软件学院, 辽宁 大连 116044
  • 收稿日期:2019-03-12 修回日期:2019-05-07 出版日期:2020-04-15 发布日期:2020-04-07
  • 作者简介:徐玲(1987-),女,博士研究生,主研方向为信息安全;乔建忠、林树宽、祁瑞华,教授、博士。
  • 基金资助:
    国家自然科学基金青年基金项目(61702072);大连外国语大学科研基金(2015XJQN05)。

Trust Model for Volunteer Computing Based on Bayesian Theorem

XU Ling1,2, QIAO Jianzhong1, LIN Shukuan1, QI Ruihua2   

  1. 1. School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China;
    2. School of Software Engineering, Dalian University of Foreign Languages, Dalian, Liaoning 116044, China
  • Received:2019-03-12 Revised:2019-05-07 Online:2020-04-15 Published:2020-04-07

摘要: 志愿计算因其开放性、匿名性和动态性得到广泛应用,但同时也对系统的安全性带来挑战。传统认证方式无法满足志愿计算系统的安全性需求,而通过在系统中建立信任机制可以有效解决这一问题。为此,构建一种基于贝叶斯定理的志愿计算系统信任模型VC-trust。依据贝叶斯定理对节点的不确定性行为进行分析预测,根据节点历史交互记录并引入处罚因子和调节函数计算节点信任值,同时利用时间滑动窗口对其进行更新。实验结果表明,在节点行为变化的情况下,VC-trust模型较BTMS模型具有更高的交互成功率。

关键词: 志愿计算, 不确定性, 信任, 时间滑动窗口, 贝叶斯定理

Abstract: Volunteer computing has been widely used for its openness,anonymity and dynamic features,but it also brings threats to system security.Traditional authentication methods cannot meet security requirements of volunteering computing systems,which can be solved by building the trust mechanism in systems.This paper proposes a trust model named VC-trust based on Bayesian theorem for volunteering computing systems.The uncertain behavior of nodes is analyzed and predicted based on Bayesian theorem.Then the trust values are calculated by introducing the punishment factor and adjustment function according to historical interactions reconds of nodes,and updated by using time sliding windows.Experimental results show that,in the case of node behavior changing,the VC-trust model has higher interaction success rate compared with BTMS model.

Key words: volunteer computing, uncertainty, trust, time sliding window, Bayesian theorem

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