P2P网络中基于动态贝叶斯网络可信度量模型

本文针对P2P网络中节点匿名、动态导致安全性下降,现有信任度模型计算动态性不足等问题,提出了基于动态贝叶斯网络的可信度量计算模型,模型依据历史交互记录数据,从直接信任度与推荐信任度两个方面进行信任度量计算,并考虑到时效性与恶意节点等问题,引入时效因子与惩罚因子.最后通过仿真实验验证了模型的有效性与可行性.

Trusted Measurement Model Based on Dynamic Bayesian Network in P2P Networks

Inspired by the deficiency of anonymous nodes, the descent of safety caused by the dynamics and the computation insufficiency of current trustworthiness mode, the paper proposes a computing mode of trust evaluation based on the dynamic Bayesian network. The mode calculates the trustworthiness from the direct trust and commendation one according to the historical interaction data. Time-effect factor and penalty factor are introduced in the paper to solve the problems of timeliness and malicious node. The efficiency and practicability of the mode are proved by the simulation experiment.