Advancements in ECU Identification and Fingerprinting for Enhanced Cybersecurity in Vehicle Networks

This paper explores novel techniques for identifying Electronic Control Units (ECUs) and detecting unauthorized messages in vehicle networks. By leveraging signal processing, machine learning, and spectral analysis, the proposed system achieves a high accuracy rate of 98.76% in classifying known ECUs and identifying rogue messages. Additionally, the paper discusses future research directions for dynamic online classification and network complexity optimization.