分布式网络的可信度评估框架

S. Hall, W. McQuay, K. Littlejohn
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引用次数: 1

摘要

本研究探讨了马尔可夫交换模型在评估异质网络(如分布式传感器网络)的信任和可信度方面的效用。隐马尔可夫模型(HMM)作为一种无监督机器学习方法,不依赖于复杂系统信任建模中常用的假设。模拟了一个相关的时间序列,从可信的时间段切换到不可信的时间段,以说明HMM理论及其在信任建模中的有效性。在本文中,我们使用HMM来估计统一信任模型的参数,该模型可以连续地确定在任何应用环境中收集的数据的可信度。结果表明,尽管输入信号中存在各种噪声和不确定性,该方法仍能有效地适应所指定的信任模型所期望的特征。本研究通过定义一个新的可信度度量标准并使用HMM,在计算成本、估计和预测的准确性、更少的先验假设和系统不可知论等方面对过去的研究进行了改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A trustworthiness evaluation framework for distributed networks
This research examines the utility of Markov switching models in assessing trust and trustworthiness of a heterogeneous network, e.g. distributed sensor networks. As an unsupervised machine learning method, hidden Markov models (HMM) is independent of the assumptions commonly used in modeling trust in complex systems. A relevant time series that switches regimes from trusted to untrusted periods of times is simulated to illustrate the theory of HMM and its effectiveness in Trust modeling. In this paper, we have employed HMM to estimate the parameters of a unified trust model that could make continual determinations of the trustworthiness of the data collected in any application environment. The results indicate that this method could effectively accommodate the desired features of our specified trust model despite various noises and uncertainties in the input signal. This study, by defining a new metric of trustworthiness and using HMM, provides an improvement over past studies in terms of computation costs, accuracy of estimation and forecasting, less a priori assumptions, and system agnosticism.
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