Preserving Data Integrity In Iot Networks Under Opportunistic Data Manipulation

Keywords

Bayesian framework; Data integrity; IoT; Prospect theory

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 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.

Publication Date

3-29-2018

Publication Title

Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017

Volume

2018-January

Number of Pages

446-453

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.87

Socpus ID

85046641008 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85046641008

This document is currently not available here.

Share

COinS