In controller area networks (CANs), electronic control units (ECUs) such as telematics ECUs and on-board diagnostic ports must protect the message exchange from spoofing attacks. In this paper, we propose a CAN bus authentication framework that exploits physical layer features of the messages, including message arrival intervals and signal voltages, and applies reinforcement learning to choose the authentication mode and parameter. By applying the Dyna architecture and using a double estimator, this scheme improves the utility in terms of authentication accuracy without changing the CAN bus protocol or the ECU components and requiring knowledge of the spoofing model. We also propose a deep learning version to further improve the authentication efficiency for the CAN bus. The learning scheme applies a hierarchical structure to reduce the exploration time, and uses two deep neural networks to compress the high-dimensional state space and to fully exploit the physical authentication experiences. We provide the computational complexity and the performance analysis. Experimental results verify the theoretical analysis and show that our proposed schemes significantly improve the authentication accuracy as compared with benchmark schemes.

中文翻译：

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基于强化学习的控制器区域网络物理层身份验证


在控制器局域网 (CAN) 中，远程信息处理 ECU 和车载诊断端口等电子控制单元 (ECU) 必须保护消息交换免受欺骗攻击。在本文中，我们提出了一种 CAN 总线身份验证框架，该框架利用消息的物理层特征（包括消息到达间隔和信号电压），并应用强化学习来选择身份验证模式和参数。通过应用Dyna架构并使用双估计器，该方案提高了认证准确性方面的实用性，而无需改变CAN总线协议或ECU组件并且不需要了解欺骗模型。我们还提出了深度学习版本，以进一步提高 CAN 总线的认证效率。该学习方案采用层次结构来减少探索时间，并使用两个深度神经网络来压缩高维状态空间并充分利用物理认证经验。我们提供计算复杂度和性能分析。实验结果验证了理论分析，并表明我们提出的方案与基准方案相比显着提高了认证精度。