A non‐biased trust model for wireless mesh networks

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

Trust based Secure Reliable Route Discovery in Wireless Mesh Networks

“…The comparison of various existing reputation models is also discussed [28]. Tan et al [29] have proposed a non-biased trust model for WMNs which combines two techniques such as dissimilarity test and Dempster Shafer Theory [30] to compute the trust values of the nodes effectively. Compared to existing trust models, this model handles fabricated trust information efficiently and also protects against badmouthing and ballot-stuffing attacks.…”