Predictive digital twin driven trust model for cloud service providers with Fuzzy inferred trust score calculation

Jomina John, John Singh K
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Abstract

Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection.
利用模糊推断信任分值计算方法为云服务提供商建立数字孪生驱动的预测性信任模型
云计算已成为现代计算基础设施不可或缺的一部分,具有可扩展性、灵活性和成本效益。信任是云计算的一个重要方面,影响着用户选择云服务提供商(CSP)的决策。本文全面回顾了云计算中现有的信任模型,包括基于协议、基于服务水平协议、基于证书、基于反馈、基于领域、基于预测和基于声誉的模型。在此基础上,我们提出了一种新颖的方法,利用 CSP 数字孪生创建云计算中的信任模型。数字孪生中增加了模糊推理系统,可根据信任相关参数计算 CSP 的信任分数。详细介绍了带有模糊推理系统的数字孪生的架构,概述了它如何处理通过渗透测试机制获得的安全参数值。这些参数值通过线性脊回归函数转化为清晰值,然后输入模糊推理系统,计算出 CSP 的最终信任度得分。本文还介绍了模糊推理系统的输出结果,展示了不同的安全参数输入如何产生不同的信任分值。该方法为评估 CSP 可信度和加强云服务选择的决策过程提供了一个稳健的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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