A major objective of vehicular networking is to improve road safety and reduce traffic congestion. The experience of individual vehicles on traffic conditions and travel situations can be shared with other vehicles for improving their route planning and driving decisions. Nevertheless, the frequent occurrence of adversary vehicles in the network may affect the overall network performance and safety. These vehicles may behave intelligently to avoid detection. To effectively control and monitor such security threats, an efficient Trust Management system should be employed to identify the trustworthiness of individual vehicles and detect malicious drivers which is the major focus of this work. We propose a hybrid solution, which integrates Edge Computing and Multi-agent modeling in a Trust Management system for vehicular networks. The proposed solution also aims to overcome the limitations of the two commonly utilized approaches in this context: cloud computing and Peer-to-Peer (P2P) networking. Our framework has a set of features that make it an efficient platform to address the major security challenges in vehicular networks including latency, scalability, uncertainty, data accessibility, and malicious behavior detection. Performance of the approach is evaluated by simulating a realistic environment. Experimental results show that the proposed approach outperforms similar approaches from literature for various performance indicators.

车辆联网的主要目的是提高道路安全性并减少交通拥堵。可以与其他车辆共享个别车辆在交通状况和行驶状况方面的经验，以改善其路线规划和驾驶决策。但是，网络中敌方车辆的频繁出现可能会影响整个网络的性能和安全性。这些车辆可能会表现得很聪明，以避免被发现。为了有效地控制和监视此类安全威胁，应使用有效的信任管理系统来识别单个车辆的可信度并检测恶意驱动程序，这是此项工作的主要重点。我们提出了一种混合解决方案，该解决方案将边缘计算和多主体建模集成到了用于车辆网络的信任管理系统中。提出的解决方案还旨在克服两种常用方法在这种情况下的局限性：云计算和对等（P2P）网络。我们的框架具有一系列功能，使其成为应对车载网络中主要安全挑战的有效平台，其中包括时延，可伸缩性，不确定性，数据可访问性和恶意行为检测。通过模拟现实环境来评估该方法的性能。实验结果表明，对于各种性能指标，该方法优于文献中的相似方法。我们的框架具有一系列功能，使其成为应对车载网络中主要安全挑战的有效平台，其中包括时延，可伸缩性，不确定性，数据可访问性和恶意行为检测。通过模拟现实环境来评估该方法的性能。实验结果表明，对于各种性能指标，该方法优于文献中的相似方法。我们的框架具有一系列功能，使其成为应对车载网络中主要安全挑战的有效平台，其中包括时延，可伸缩性，不确定性，数据可访问性和恶意行为检测。通过模拟现实环境来评估该方法的性能。实验结果表明，对于各种性能指标，该方法优于文献中的相似方法。