Visible to the public Voltage Based Authentication for Controller Area Networks with Reinforcement Learning

TitleVoltage Based Authentication for Controller Area Networks with Reinforcement Learning
Publication TypeConference Paper
Year of Publication2019
AuthorsXu, Tangwei, Lu, Xiaozhen, Xiao, Liang, Tang, Yuliang, Dai, Huaiyu
Conference NameICC 2019 - 2019 IEEE International Conference on Communications (ICC)
Date Publishedmay
Keywordsauthentication mode, authorisation, CAN bus, Computer architecture, computer network security, Computers, controller area network security, controller area networks, Cyber-physical systems, ECU signals, electronic control units, Internet of Things, learning (artificial intelligence), linear regression, machine learning, Neural networks, physical authentication scheme, pubcrawl, reinforcement learning, Resiliency, spoofing attacks, spoofing model, Support vector machines, Testing
AbstractController 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.
Citation Keyxu_voltage_2019