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A Prospect Theoretic Approach for Trust Management in IoT Networks Under Manipulation Attacks

机译：操纵攻击中IOT网络信任管理的前景理论方法

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摘要

As Internet of Things (loT) and Cyber-Physical systems become more ubiquitous in our daily lives, it necessitates the capability to measure the trustworthiness of the aggregate data from such systems to make fair decisions. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application. In addition, there exist varying levels of uncertainty associated with an evidence upon which a trust model is built. Hence, the data integrity scoring mechanisms require some provisions to adapt to different risk attitudes and uncertainties.In this article, we propose a prospect theoretic framework for data integrity scoring that quantifies the trustworthiness of the collected data from IoT devices in the presence of adversaries who try to manipulate the data. In our proposed method, we consider an imperfect anomaly monitoring mechanism that tracks the transmitted data from each device and classifies the outcome (trustworthiness of data) as not compromised, compromised, or undecided. These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters. These parameters are then used for calculating a utility value via prospect theory to evaluate the reliability of the aggregate data at an IoT hub. In addition, to take into account different risk attitudes, we propose two types of fusion rule at IoT hub-optimistic and conservative.Furthermore, we put forward asymmetric weighted moving average scheme to measure the trustworthiness of aggregate data in presence of On-Off attacks. The proposed framework is validated using extensive simulation experiments for both uniform and On-Off attacks. We show how trust scores vary under a variety of system factors like attack magnitude and inaccurate detection. In addition, we measure the trustworthiness of the aggregate data using the well-known expected utility theory and compare the results with that obtained by prospect theory. The simulation results reveal that prospect theory quantifies trustworthiness of the aggregate data better than expected utility theory.
机译：随着物联网（地段）和网络物理系统在我们的日常生活中变得更加普遍存在，需要能够测量来自这些系统的总数据的可信度，以进行公平的决策。但是，对可靠性的解释是语境性的，并根据有关申请的风险容忍态度而有所不同。此外，与建立信任模型的证据相关的不确定性存在不同的不确定性。因此，数据完整性评分机制需要一些规定，以适应不同的风险态度和不确定性。在本文中，我们向数据完整性评分提出了一个展望理论框架，这些框架可以在存在对手的情况下量化来自IoT设备的收集数据的可信度。尝试操纵数据。在我们提出的方法中，我们考虑了一个不完美的异常监测机制，可以从每个设备跟踪传输的数据，并将结果（数据值得信赖性）分类为不妥协，损害或未确定。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，这些参数通过前景理论计算实用程序值，以评估IOT集线器的聚合数据的可靠性。此外，要考虑到不同的风险态度，我们提出了两种类型的融合规则，在IOT集线器乐观和保守。繁殖，我们提出了非对称加权移动平均方案来测量总数据存在的终结数据的可信度。使用广泛的仿真实验验证所提出的框架，用于均匀和开关攻击。我们展示了信赖评分在攻击幅度等各种系统因素下如何变化，如攻击幅度和检测不准确。此外，我们使用众所周知的预期实用理论来测量总数据的可信度，并将结果与​​前景理论获得的结果进行比较。仿真结果表明，前景理论比预期的实用理论更好地量化总数据的可信度。

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