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Machine learning based trust management framework for vehicular networks

机译：基于机器学习的车辆网络信任管理框架

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摘要

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. (C) 2020 Elsevier Inc. All rights reserved.
机译：在车辆网络中建立安全指标仍在讨论。车辆网络的动态特性强加了实现适当的解决方案来组织和确保在车辆节点之间进行可靠的数据传输的挑战。为了确保道路安全，避免/减少交通拥堵，并识别恶意车辆，必须在实时实现有效的信任管理系统。该区域的所有现有应用程序都集中在传送消息的可靠数据交换和认证过程上。本研究提出了一种使用决策树分类和人工神经网络的新实体中心信任框架。决策树分类模型用于导出信任计算规则，并且当不满足预期信任值时，使用人工神经网络自动列出车辆节点。此模型使用多方面的角色和基于距离的距离，如欧几里德距离来估计信任。所提出的实体中心信任模型，使用多功能的新直接和推荐的信任评估策略来计算信任值。与其他流行的实体中心信任模型相比，建议的模型简单，可靠，有效。进行了结果和比较分析，以证明拟议模型更好地进行其他相关方法。 （c）2020 Elsevier Inc.保留所有权利。

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