Towards a Comprehensive Evaluation of Voltage-Based Fingerprinting for the CAN Bus

The Controller Area Network (CAN), a standard communication protocol in modern vehicles, lacks inherent security features, making it susceptible to attacks. While various defense mechanisms have been proposed, their practical implementation in resourceconstrained vehicles remains limited. This paper presents a comprehensive evaluation framework for voltage-based fingerprinting, a promising technique for identifying and mitigating CAN bus attacks. This framework compares the performance of four different machine learning (ML) models, analyzes the impact of distinct sections within the CAN voltage frame, explores various waveform and feature types, and considers practical deployment factors such as detection latency and sampling rate. Notably, the paper investigates the CAN ringing phenomenon and its potential for efficient Electronic Control Unit (ECU) identification. Results demonstrate that the proposed framework offers robust classification performance while ensuring real-world feasibility.