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.

Mobile edge computing systems help improve the performance of computational-intensive applications on mobile devices and have to resist jamming attacks and heavy interference. In this paper, we present a reinforcement learning based mobile offloading scheme for edge computing against jamming attacks and interference, which uses safe reinforcement learning to avoid choosing the risky offloading policy that fails to meet the computational latency requirements of the tasks. This scheme enables the mobile device to choose the edge device, the transmit power and the offloading rate to improve its utility including the sharing gain, the computational latency, the energy consumption and the signal-to-interference-plus-noise ratio of the offloading signals without knowing the task generation model, the edge computing model, and the jamming/interference model. We also design a deep reinforcement learning based mobile offloading for edge computing that uses an actor network to choose the offloading policy and a critic network to update the actor network weights to improve the computational performance. We discuss the computational complexity and provide the performance bound that consists of the computational latency and the energy consumption based on the Nash equilibrium of the mobile offloading game. Simulation results show that this scheme can reduce the computational latency and save energy consumption.

Controller area networks (CANs) are vulnerable to spoofing attacks such as frame falsifying attacks, as electronic control units (ECUs) send and receive messages without any authentication and encryption. In this paper, we propose a physical authentication scheme that exploits the voltage features of the ECU signals on the CAN bus and applies reinforcement learning to choose the authentication mode such as the protection level and test threshold. This scheme enables a monitor node to optimize the authentication mode via trial-and-error without knowing the CAN bus signal model and spoofing model. Experimental results show that the proposed authentication scheme can significantly improve the authentication accuracy and response compared with a benchmark scheme.