As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous and an integral part of our daily lives, it is important that we are able to trust the data aggregate from such systems. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application and varying levels of uncertainty associated with the evidence upon which trust models act. Hence, the data integrity scoring mechanisms should have provisions to adapt to varying risk attitudes and uncertainties. In this paper, we propose a Bayesian inference model and a prospect theoretic framework for data integrity scoring that quantify the trustworthiness of data collected from IoT devices in the presence of an adversaries who manipulate the data. We consider an imperfect anomaly monitoring mechanism that monitors the data being sent from each device and classifies the outcome as not compromised, compromised, and cannot be inferred. These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters which are then used for calculating a utility value on how reliable the aggregate data is. We use a prospect theory inspired approach to quantify this data integrity score and evaluate the trustworthiness of the aggregate data from the IoT framework. Furthermore, we also model the system using the traditionally used expected utility theory and compare the results with that obtained using prospect theory. As decisions are based on how the data is fused, we propose two measuring models: one optimistic and another conservative. The proposed framework is validated using extensive simulation experiments. We show how data integrity scores vary under a variety of system factors like attack intensity and inaccurate detection.

中文翻译：

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操纵攻击下物联网网络信任管理的前景理论方法

随着物联网 (IoT) 和网络物理系统变得越来越普遍并成为我们日常生活中不可或缺的一部分，因此能够信任来自此类系统的数据聚合非常重要。然而，对可信度的解释是上下文相关的，并且根据相关应用程序的风险容忍态度以及与信任模型所依据的证据相关的不同程度的不确定性而有所不同。因此，数据完整性评分机制应具有适应不同风险态度和不确定性的规定。在本文中，我们提出了一个贝叶斯推理模型和一个用于数据完整性评分的前景理论框架，用于量化在存在操纵数据的对手的情况下从物联网设备收集的数据的可信度。我们考虑了一种不完善的异常监控机制，该机制监控从每个设备发送的数据，并将结果分类为未受损、受损和无法推断。这些结果被概念化为具有三个参数的贝叶斯推理模型的多项假设，然后用于计算关于聚合数据的可靠性的效用值。我们使用受前景理论启发的方法来量化此数据完整性评分，并评估来自 IoT 框架的聚合数据的可信度。此外，我们还使用传统使用的预期效用理论对系统进行建模，并将结果与​​使用前景理论获得的结果进行比较。由于决策是基于数据的融合方式，我们提出了两种衡量模型：一种是乐观的，另一种是保守的。使用广泛的模拟实验验证了所提出的框架。我们展示了数据完整性分数在各种系统因素（如攻击强度和不准确检测）下的变化情况。