Towards Automatically Reverse Engineering Vehicle Diagnostic Protocols

Authors: 

Le Yu, Yangyang Liu, Pengfei Jing, Xiapu Luo, Lei Xue, and Kaifa Zhao, The Hong Kong Polytechnic University; Yajin Zhou, Zhejiang University; Ting Wang, The Pennsylvania State University; Guofei Gu, Texas A&M University; Sen Nie and Shi Wu, Tencent Keen Security Lab

Abstract: 

In-vehicle protocols are very important to the security assessment and protection of modern vehicles since they are used in communicating with, accessing, and even manipulating ECUs (Electronic Control Units) that control various vehicle components. Unfortunately, the majority of in-vehicle protocols are proprietary without publicly-available documentations. Although recent studies proposed methods to reverse engineer the CAN protocol used in the communication among ECUs, they cannot be applied to vehicle diagnostics protocols, which have been widely exploited by attackers to launch remote attacks. In this paper, we propose a novel framework for automatically reverse engineering the diagnostic protocols by leveraging professional diagnostic tools for vehicles. Specifically, we design and develop a new cyber-physical system that uses a set of algorithms to control a programmable robotics arm with the aid of cameras to automatically trigger and capture the messages of diagnostics protocols as well as reverse engineer their formats, semantic meanings, proprietary formulas used for processing the response messages. We perform a large scale experiment to evaluate our prototype by using 18 real vehicles. It successfully reverses engineers 570 messages (446 for reading sensor values and 124 for controlling components). The experimental results show that our framework achieves high precision in reverse engineering proprietary formulas and obtains much more messages than the prior approach based on app analysis.

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BibTeX
@inproceedings {277266,
title = {Towards Automatically Reverse Engineering Vehicle Diagnostic Protocols},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
address = {Boston, MA},
url = {https://www.usenix.org/conference/usenixsecurity22/presentation/yu-le},
publisher = {USENIX Association},
month = aug,
}