Vehicular ad hoc network (VANET) has become an accessible technology for improving road safety and driving experience, the problems of heterogeneity and lack of resources it faces have also attracted widespread attention. With the development of software-defined networking (SDN) and multiaccess edge computing (MEC), a variety of resource allocation strategies in MEC-enabled software-defined networking-based VANET (SDVN) have been proposed to solve these problems. However, we note that few of these work involves the situation where SDVN is under Distributed Denial of Service (DDoS) attacks. Actually, Internet of Things (IoT) devices are extremely easy to be compromised by malicious users, and compromised IoT devices may be used to launch edge DDoS attacks against the MEC servers in MEC-enabled SDVN at any time. In this article, we propose a graph neural network (GNN)-based collaborative deep reinforcement learning (GCDRL) model to generate the resource provisioning and mitigating strategy. The model evaluates the trust value of the vehicles, formulates mitigation of edge DDoS attacks and resource provisioning strategies to ensure that the MEC servers can work normally under edge DDoS attacks. In addition, GNN is adopted in the DRL model to extract the structure feature of the graph composed of MEC servers, and help transfer computing tasks between MEC servers to alleviate the problem of resources imbalance between them. Experimental results show that the method of estimating the vehicular trust value is effective, and our method can make the average throughput of edge nodes more stable and lower down the average delay and the average energy consumption under the edge DDoS attack. Also, a real-world case study is conducted to verify our conclusion.

中文翻译：

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用于缓解启用 MEC 的 SDVN 中的边缘 DDoS 攻击的资源配置


车载自组织网络（VANET）已成为改善道路安全和驾驶体验的一项无障碍技术，其面临的异构性和资源匮乏的问题也引起了广泛关注。随着软件定义网络（SDN）和多路访问边缘计算（MEC）的发展，人们提出了基于MEC的基于软件定义网络的VANET（SDVN）中的多种资源分配策略来解决这些问题。然而，我们注意到这些工作很少涉及 SDVN 遭受分布式拒绝服务 (DDoS) 攻击的情况。事实上，物联网（IoT）设备极易被恶意用户攻破，被攻破的物联网设备随时可能被用来对支持MEC的SDVN中的MEC服务器发起边缘DDoS攻击。在本文中，我们提出了一种基于图神经网络（GNN）的协作深度强化学习（GCDRL）模型来生成资源供应和缓解策略。该模型评估车辆的信任值，制定边缘DDoS攻击缓解和资源配置策略，确保MEC服务器在边缘DDoS攻击下正常工作。此外，DRL模型中采用GNN来提取MEC服务器组成的图的结构特征，帮助MEC服务器之间转移计算任务，缓解它们之间的资源不平衡问题。实验结果表明，该估计车辆信任值的方法是有效的，并且该方法可以使边缘节点的平均吞吐量更加稳定，降低边缘DDoS攻击下的平均延迟和平均能耗。此外，还进行了现实世界的案例研究来验证我们的结论。