A non-biased trust model for wireless mesh networks

TL;DR

This paper introduces a non-biased trust model for wireless mesh networks that effectively resists reputation attacks by considering all recommendation trusts and employing dissimilarity tests with Dempster-Shafer Theory.

Contribution

It proposes a novel trust model combining dissimilarity tests and Dempster-Shafer Theory to improve robustness against reputation-based attacks.

Findings

Robust against reputation attacks compared to existing methods

Effective in mitigating blackhole and grayhole attacks in simulations

Validated through extensive NS-3 network simulations

Abstract

Trust models that rely on recommendation trusts are vulnerable to badmouthing and ballot-stuffing attacks. To cope with these attacks, existing trust models use different trust aggregation techniques to process the recommendation trusts and combine them with the direct trust values to form a combined trust value. However, these trust models are biased as recommendation trusts that deviate too much from one's own opinion are discarded. In this paper, we propose a non-biased trust model that considers every recommendation trusts available regardless they are good or bad. Our trust model is based on a combination of 2 techniques: the dissimilarity test and the Dempster-Shafer Theory. The dissimilarity test determines the amount of conflict between 2 trust records, whereas the Dempster-Shafer Theory assigns belief functions based on the results of the dissimilarity test. Numerical results show that our trust model is robust against reputation-based attacks when compared to trust aggregation techniques such as the linear opinion pooling, subjective logic model, entropy-based probability model, and regression analysis. In addition, our model has been extensively tested using network simulator NS-3 in an Infrastructure-based wireless mesh networks and a Hybrid-based wireless mesh networks to demonstrate that it can mitigate blackhole and grayhole attacks.