Mobile edge computing in vehicular ad hoc networks (VANETs) suffers from rogue edge attacks due to the vehicle mobility and the network scale. In this paper, we present a physical authentication scheme to resist rogue edge attackers whose goal is to send spoofing signals to attack VANETs. This authentication scheme exploits the channel states of the shared ambient radio signals of the mobile device and its serving edge such as the onboard unit during the same moving trace and applies reinforcement learning to select the authentication modes and parameters. By applying transfer learning to save the learning time and applies deep learning to further improve the authentication performance, this scheme enables mobile devices in VANETs to optimize their authentication modes and parameters without being aware of the VANET channel model, the packet generation model, and the spoofing model. We provide the convergence bound such as the mobile device utility, evaluate the computational complexity of the physical authentication scheme, and verify the analysis results via simulations. Simulation and experimental results show that this scheme improves the authentication accuracy with reduced energy consumption against rogue edge attacks.

中文翻译：

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基于强化学习的 VANET PHY 身份验证

由于车辆的移动性和网络规模，车辆自组织网络 (VANET) 中的移动边缘计算遭受流氓边缘攻击。在本文中，我们提出了一种物理身份验证方案，以抵抗流氓边缘攻击者，其目标是发送欺骗信号来攻击 VANET。该身份验证方案利用移动设备及其服务边缘（例如车载单元）在同一移动轨迹期间共享的环境无线电信号的信道状态，并应用强化学习来选择身份验证模式和参数。该方案通过应用迁移学习来节省学习时间并应用深度学习进一步提高认证性能，使VANET中的移动设备能够在不知道VANET信道模型的情况下优化其认证模式和参数，数据包生成模型和欺骗模型。我们提供了移动设备效用等收敛边界，评估了物理认证方案的计算复杂性，并通过模拟验证了分析结果。仿真和实验结果表明，该方案提高了认证精度，降低了对流氓边缘攻击的能耗。