Internet of Things (IoT) may be defined as a network of smart devices that are involved in data collection and exchange. This technology has automated the day-to-day jobs and thus made our lives easier. But, real-time analysis of data is not always possible in a typical cloud-IoT architecture, especially for latency-sensitive applications. This led to the introduction of fog computing. On one side, fog layer has the capability of data processing and computation at the network edge and thus provides faster results. But, on the other hand, it also brings the attack surface closer to the devices. This makes the sensitive data on the layer vulnerable to attacks. Thus, considering Fog-IoT security is of prime importance. The security of a system or platform depends upon multiple factors. The order of selection of these factors plays a vital role in efficient assessment of security. This makes the problem of assessment of Fog-IoT security a Multi-Criteria Decision-Making (MCDM) problem. Therefore, the authors have deployed an Interval-Valued Intuitionistic Fuzzy Set (IVIFS) based Analytical Hierarchy Process (AHP) for the said environment. Using this integrated approach, the Fog-IoT security factors and their sub-factors are prioritized and ranked. The results obtained using above hybrid approach are validated by comparing them with Fuzzy-AHP (F-AHP) and Classical- AHP (C-AHP) results and are found to statistically correlated. The ideology and results of this research will help the security practitioners in accessing the security of Fog-IoT environment effectively. Moreover, the outcome of this analysis will help in paving a path for researchers by shifting their focus towards the most prioritized factor thereby assuring security in the environment.

物联网（IoT）可以定义为参与数据收集和交换的智能设备网络。这项技术使日常工作自动化，从而使我们的生活更轻松。但是，在典型的云物联网架构中，尤其是对于延迟敏感的应用程序，并非总是可以实时分析数据。这导致了雾计算的引入。一方面，雾层具有在网络边缘进行数据处理和计算的能力，因此提供了更快的结果。但是，另一方面，它也使攻击面更靠近设备。这使得该层上的敏感数据容易受到攻击。因此，考虑Fog-IoT安全至关重要。系统或平台的安全性取决于多个因素。这些因素的选择顺序在安全性的有效评估中起着至关重要的作用。这使对Fog-IoT安全的评估问题成为多标准决策制定（MCDM）问题。因此，作者为上述环境部署了基于区间值直觉模糊集（IVIFS）的分析层次过程（AHP）。使用这种集成方法，将对Fog-IoT安全因素及其子因素进行优先级排序。通过将上述混合方法获得的结果与Fuzzy-AHP（F-AHP）和Classical-AHP（C-AHP）结果进行比较，验证了结果，并发现它们之间存在统计相关性。这项研究的思想和结果将帮助安全从业人员有效地访问Fog-IoT环境的安全性。而且，