A non-biased trust model for wireless mesh networks

Trust models that rely on recommendation trusts are vulnerable to badmouthing and ballot-stuffing attacks. To cope with these attacks, existing trust models employ 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 two techniques: the dissimilarity test and the Dempster Shafer Theory. The dissimilarity test determines the amount of conflict between two 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 WMN and a Hybrid based WMN to demonstrate that it can mitigate blackhole and grayhole attacks. Keywords—Wireless Mesh Networks, Information Fusion, Recommendation based Trust Model, Reputation based Attacks, Packet Dropping Attacks, Dempster’s Rule of Combination