The exchange of correct and reliable data among legitimate nodes is one of the most important challenges in vehicular ad hoc networks (VANETs). Malicious nodes and obstacles, by generating inaccurate information, have a negative impact on the security of 5G-VANET. The big data generated in the vehicular network is also an issue in the security of VANET. To this end, a security model based on authentication and plausibility is proposed to improve the safety of network named ‘AFPM’. In the first layer, an authentication mechanism using edge nodes along with 5G is proposed to deal with the illegitimate nodes who enter the network and broadcast wrong information. In the authentication mechanism, because of the growth of the connected vehicles to the edge nodes that lead to generating big data and hence the inappropriateness of the traditional data structures, cuckoo filter, as a space-efficient probabilistic data structure, is used. In the second layer, a plausibility model by performing fuzzy logic is presented to cope with inaccurate information. The plausibility model is based on detection of inconsistent data involved in the event message. The plausibility model not only tackles with inaccurate, incomplete, and inaccuracy data but also deals with misbehaviour nodes under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. All obtained results are validated through well-known evaluation measures such as F-measure and communication overhead. The results presented in this paper demonstrate that the proposed security model possesses a better performance in comparison with the existing studies.

合法节点之间正确和可靠数据的交换是车载自组织网络（VANET）面临的最重要挑战之一。恶意节点和障碍物通过生成不正确的信息，会对5G-VANET的安全性产生负面影响。车载网络中生成的大数据也是VANET的安全性问题。为此，提出了一种基于认证和真实性的安全模型，以提高名为“ AFPM”的网络的安全性。在第一层中，提出了一种使用边缘节点和5G的身份验证机制来处理进入网络并广播错误信息的非法节点。在身份验证机制中，由于连接的车辆到边缘节点的增长导致生成大数据，因此传统数据结构的不适当性，使用了布谷鸟过滤器作为空间效率高的概率数据结构。在第二层中，提出了通过执行模糊逻辑的似真性模型来应对不准确的信息。合理性模型基于事件消息中涉及的不一致数据的检测。真实性模型不仅处理不准确，不完整和不准确的数据，而且还处理视线（LOS）和非视线（NLOS）条件下的不良行为节点。所有获得的结果均通过F-措施和通信开销等众所周知的评估方法进行验证。