Establishing security metrics in vehicular networking is still being debated. The dynamic characteristics of vehicular networks, imposes challenges to realize an appropriate solution to organize and ensure reliable data transfer between the vehicular nodes. In order to ensure road safety, avoid/reduce traffic congestion, and to identify malicious vehicles, an efficient Trust Management System has to be implemented in real time scenarios. All existing applications in this area have focused on reliable data exchange and authentication process of vehicular nodes to forward messages. This study proposes a new entity centric trust framework using decision tree classification and artificial neural networks. Decision tree classification model is used to derive rules for trust calculation and artificial neural networks are used to self-train the vehicular nodes, when expected trust value is not met. This model uses multifaceted role and distance based metrics like Euclidean distance to estimate the trust. The proposed entity centric trust model, uses a versatile new direct and recommended trust evaluation strategy to compute trust values. The suggested model is simple, reliable and efficient in comparison to the other popular entity centric trust models. Results and comparative analyses are carried out to prove the better performance of the proposed model over other related approaches.

在车载网络中建立安全性度量标准仍在争论中。车辆网络的动态特性给实现组织和确保车辆节点之间可靠数据传输的适当解决方案提出了挑战。为了确保道路安全，避免/减少交通拥堵并识别恶意车辆，必须在实时方案中实施有效的信任管理系统。该领域中的所有现有应用都集中于车辆节点转发消息的可靠数据交换和身份验证过程。这项研究提出了一种使用决策树分类和人工神经网络的新的以实体为中心的信任框架。当不满足预期的信任值时，使用决策树分类模型来导出信任计算规则，并使用人工神经网络对车辆节点进行自训练。该模型使用多方面的角色和基于距离的度量标准（例如欧几里得距离）来估计信任。所提出的以实体为中心的信任模型，使用一种通用的新的直接和推荐的信任评估策略来计算信任值。与其他流行的以实体为中心的信任模型相比，建议的模型简单，可靠且高效。进行了结果和比较分析，以证明该模型优于其他相关方法的性能。所提出的以实体为中心的信任模型，使用一种通用的新的直接和推荐的信任评估策略来计算信任值。与其他流行的以实体为中心的信任模型相比，建议的模型简单，可靠且高效。进行了结果和比较分析，以证明该模型优于其他相关方法的性能。所提出的以实体为中心的信任模型，使用一种通用的新的直接和推荐的信任评估策略来计算信任值。与其他流行的以实体为中心的信任模型相比，建议的模型简单，可靠且高效。进行了结果和比较分析，以证明该模型优于其他相关方法的性能。