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

TL;DR

This paper introduces a novel trust management framework for IoT networks that employs a prospect theoretic approach to assess data integrity under manipulation attacks, accounting for uncertainty and varying risk attitudes.

Contribution

It presents a new Bayesian inference and prospect theory-based model for data trustworthiness, incorporating imperfect anomaly detection and comparing with traditional utility models.

Findings

Data integrity scores vary with attack intensity and detection accuracy.

Prospect theory provides a different trust assessment compared to expected utility theory.

The framework is validated through extensive simulations.

Abstract

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.