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首页> 外文期刊>Eurasip Journal on Wireless Communications and Networking >A security authentication method based on trust evaluation in VANETs
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A security authentication method based on trust evaluation in VANETs

机译:VANET中基于信任评估的安全认证方法

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Vehicular ad hoc networks (VANETs) have a high degree of openness. Therefore, if a new vehicle node wants to access the network, we need to validate the vehicle node carefully to ensure the security of the entire networking. There are a large number of vehicle nodes, and the strange degree among the nodes is very high in VANETs. In addition, VANETs are human-oriented networks. All vehicle nodes in the VANET have the right to decide whether to accept a new node. To attack this challenge, this paper proposes a security authentication method based on trust evaluation. The security authentication method consists of two parts: secure authentication based on direct trust evaluation and secure authentication based on indirect trust evaluation. In the direct trust evaluation, the security vector model is established based on the security behaviors of the new vehicle node. The historical security evaluation from the authority units (AU) is collected to calculate the final direct trust. In the indirect trust evaluation, the trust degree is calculated based on the recommendation trust vectors from the vehicle nodes in the network. The method employs correlation coefficient for distinguishing the malicious vehicle. Then, the recommendations from the malicious vehicle nodes are removed. The final recommended trust is gained by calculating the average recommendation trusts of remained vehicle nodes. Simulation results show the advantage of our proposed method. Keywords VANETs Direct trust Indirect trust Correlation coefficient Security
机译:车载自组织网络(VANET)具有高度的开放性。因此,如果新的车辆节点要访问网络,则需要仔细验证车辆节点以确保整个网络的安全性。车辆节点众多,在VANET中节点间的陌生度很高。另外,VANET是面向人的网络。 VANET中的所有车辆节点均有权决定是否接受新节点。为了应对这一挑战,本文提出了一种基于信任评估的安全认证方法。安全认证方法包括两部分:基于直接信任评估的安全认证和基于间接信任评估的安全认证。在直接信任评估中,基于新车辆节点的安全行为建立安全矢量模型。收集来自授权单位(AU)的历史安全评估以计算最终的直接信任。在间接信任评估中,基于来自网络中车辆节点的推荐信任向量来计算信任度。该方法采用相关系数来区分恶意车辆。然后,删除来自恶意车辆节点的建议。通过计算剩余车辆节点的平均推荐信任度来获得最终的推荐信任度。仿真结果表明了该方法的优越性。 VANETs直接信任间接信任相关系数安全性

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