ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks

Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. The controller area network (CAN) protocol is used for communication between in-vehicle control networks (IVN). The absence of basic security features of this protocol, like message authentication, makes it quite vulnerable to a wide range of attacks including spoofing attacks. As traditional cybersecurity methods impose limitations in ensuring confidentiality and integrity of transmitted messages via CAN, a new technique has emerged among others to approve its reliability in fully authenticating the CAN messages. At the physical layer of the communication system, the method of fingerprinting the messages is implemented to link the received signal to the transmitting electronic control unit (ECU). This paper introduces a new method to implement the security of modern electric vehicles. The lumped element model is used to characterize the channel-specific step response. ECU and channel imperfections lead to a unique transfer function for each transmitter. Due to the unique transfer function, the step response for each transmitter is unique. In this paper, we use control system parameters as a feature-set, afterward, a neural network is used transmitting node identification for message authentication. A dataset collected from a CAN network with eight-channel lengths and eight ECUs to evaluate the performance of the suggested method. Detection results show that the proposed method achieves an accuracy of 97.4% of transmitter detection.