Title: IoT trust aggregation using hybrid outlier detection and consensus

Authors: Vishwanath G. Garagad; Nalini C. Iyer

Addresses: School of Electronics and Communication Engineering, KLE Technological University, Hubli, Karnataka, India ' School of Electronics and Communication Engineering, KLE Technological University, Hubli, Karnataka, India

Abstract: Trust modelling and management strategy used identify and mitigate threats by malicious devices rely on peer recommendations to compute trustworthiness. Aggregating opinions from independent devices is crucial in such recommendation-based systems to arrive at a consensus for decision making. Existing aggregation techniques like arithmetic mean, geometric mean (weighted/non-weighted), and maximum/minimum functions ignore the risk of biased and uncertain recommendations. To encounter such vulnerabilities, we propose a novel trust assessment model, outlier and uncertain recommendation-based trust management (OUR-Trust). It uses an outlier elimination and similarity-based scheme to evaluate the recommender's credibility before aggregation for consensus and decision making. The model employs revised Dempster-Shaffer combination rule for aggregation, which considers the uncertainty factor. Effectiveness of proposed approach is analysed for a dynamic and heterogeneous IoT network of dynamic and heterogeneous devices. OUR-Trust is validated for storage, power-efficiency, and scalability in terms of convergence time for more extensive IoT networks that employ recommender systems.

Keywords: trust management; distributed model; reputation system; recommender system; median absolute deviation; outlier detection; uncertainty; aggregation; consensus.

DOI: 10.1504/IJSNET.2023.130705

International Journal of Sensor Networks, 2023 Vol.41 No.4, pp.229 - 243

Received: 29 Jun 2022
Accepted: 16 Jan 2023
Published online: 03 May 2023 *

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