一种无线mesh网络的无偏信任模型
A non-biased trust model for wireless mesh networks
摘要：
依赖于推荐信任的信任模型容易受到恶意中伤和投票填充攻击。为了应对这些攻击，现有的信任模型使用不同的信任聚合技术来处理推荐信任，并将它们与直接信任值组合，形成组合信任值。然而，这些信任模型是有偏见的，因为推荐信任偏离自己的意见太多被丢弃。在本文中，我们提出了一个无偏见的信任模型，认为每一个推荐信任可用，无论他们是好还是坏。我们的信任模型是基于2种技术的组合：相异性测试和Dempster-Shafer理论。相异性测试确定2个信任记录之间的冲突量，而Dempster-Shafer理论基于相异性测试的结果分配置信函数。数值结果表明，我们的信任模型是强大的，对基于声誉的攻击相比，信任聚合技术，如线性意见池，主观逻辑模型，基于熵的概率模型，回归分析。此外，我们的模型已被广泛测试，使用网络模拟器NS-3在一个基于网络结构的无线mesh网络和一个基于混合的无线mesh网络，以证明它可以减轻黑洞和灰洞攻击。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
