Voltage Based Electronic Control Unit (ECU) Identification with Convolutional Neural Networks and Walsh Hadamard Transform

This paper proposes an identification approach for the Electronic Control Units (ECU) in the vehicle, which is based on the physical characteristics of the ECU extracted from their voltage output. Then, the identification is not based on cryptographic means but it could be used as an alternative or complementary to strengthen cryptographic solutions for vehicle cybersecurity. While previous research has used hand-crafted features like mean voltage, max voltage, skew or variance this study applies Convolutional Neural Network in combination with the Walsh Hadamard Transform, which has interesting properties of compactness and robustness to noise. The approach is applied to a recently public data set of ECU voltage fingerprints. The results show that the combination of CNN and the Walsh Hadamard Transform outperforms, in terms of identification accuracy, robustness to noise and computing times, other approaches proposed in literature based on shallow machine learning and tailor made features, CNN with other linear transforms (Discrete Fourier Transform or Discrete Hartley Transform) or CNN with the original time domain representations.

BALDINI Gianmarco; 

2023-02-14

MDPI

JRC131847

2079-9292 (online),   

https://www.mdpi.com/2079-9292/12/1/199,    https://publications.jrc.ec.europa.eu/repository/handle/JRC131847,   

10.3390/electronics12010199 (online),