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Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET

机译:具有机器学习方案的轻量级信任模型,以便在VANET中保护隐私

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A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET.
机译:车辆临时网络(VANET)正在将公共交通转换为更安全的无线网络,提高其安全性和效率。 VANET由几个节点组成,包括RSU(路边单元),车辆,交通信号和正在传送网络中的敏感信息的其他无线通信设备。 然而,由于依赖Vanet中使用的网络基础设施,动态性质和控制技术,安全威胁日益增加。 通过在公路运输节点上使用机器学习和人工智能,可以广泛解决安全威胁。 本文基于VANET的应用和安全要求,提出了信任和加密的比较。

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