Preserving Data Integrity in IoT Networks Under Opportunistic Data Manipulation

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 by a hub in the presence of an adversary manipulating data and an imperfect anomaly monitoring mechanism. The monitoring mechanism 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 prospect theory inspired approach to quantify this data integrity score and evaluate trustworthiness of the aggregate data from the IoT framework. 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.