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Subjective logic based trust model for geographic routing in mobile ad hoc networks

机译:Ad hoc网络中基于主观逻辑的地理路由信任模型

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In the past decades, several trust models have been proposed to enhance the security of Mobile Ad hoc Networks (MANETs). The conventional trust models fail to express the notion of uncertainty during the establishment of trust relationships between mobile nodes. Furthermore, they lack in considering the context attributes in trust evaluation. This paper proposes a subjective logic based trust model in POR (SLT-POR) that integrates both the behavioural and context based trust in trust model. The behavioural based trust incorporates subjective logic based evidence fusion in indirect trust evaluation to explicitly represent and manage ignorance as uncertainty. The trust relationship between nodes cannot always reflect the actual relationship and consequently the executed decision from the extracted trust relationship is not always accurate. The subjective logic theory includes a dynamic base rate operator that evaluates an expectation of an opinion and handles the uncertainty in evidence collection. In context, attributes based trust measurement, the SLT-POR estimates a context based trust value using the contextual attributes of each node in the trusted positive progress set. The SLT-POR considers the battery power to include node’s lifetime in the trustworthiness estimation. It assigns a weight for both behaviour and context based trust to efficiently calculate total trustworthiness of a node. The simulation results demonstrate that the packet delivery ratio of the proposed SLT-POR is high even if 50 % of total nodes are malicious and outperforms Context-Aware Security and Trust (CAST) framework.
机译:在过去的几十年中,已经提出了几种信任模型来增强移动自组织网络(MANET)的安全性。传统的信任模型无法在移动节点之间建立信任关系时表达不确定性的概念。此外,他们在信任评估中缺乏考虑上下文属性的能力。本文提出了一种基于主观逻辑的POR信任模型(SLT-POR),该模型将行为和基于上下文的信任集成在信任模型中。基于行为的信任在间接信任评估中结合了基于主观逻辑的证据融合,以明确表示和管理无知作为不确定性。节点之间的信任关系不能总是反映实际关系,因此从提取的信任关系中执行的决策并不总是准确的。主观逻辑理论包括一个动态的基本费率算子,该算子评估意见的期望并处理证据收集中的不确定性。在基于属性的上下文信任度度量中,SLT-POR使用可信肯定进度集中每个节点的上下文属性估计基于上下文的信任值。 SLT-POR认为电池电量将节点的寿命包括在可信度估计中。它为基于行为的信任和基于上下文的信任分配权重,以有效地计算节点的总信任度。仿真结果表明,即使总节点的50%是恶意的,所提出的SLT-POR的数据包传递率也很高,并且性能优于上下文感知的安全和信任(CAST)框架。

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