NDSS

Automated Cross-Platform Reverse Engineering of CAN Bus Commands From Mobile Apps

Haohuang Wen (The Ohio State University), Qingchuan Zhao (The Ohio State University), Qi Alfred Chen (University of California, Irvine), Zhiqiang Lin (The Ohio State University)

In modern automobiles, CAN bus commands are necessary for a wide range of functionalities such as diagnosis, security monitoring, and recently autonomous driving. However, their specifications are developed privately by car manufacturers, and today the most effective way of revealing the proprietary CAN bus commands is to reverse engineer (e.g., dynamic test) with real cars, which is time consuming, costly, and error-prone. In this paper, we propose a cost-effective (no real car needed) and automatic (no human intervention required) approach for reverse engineering CAN bus commands using just car companion mobile apps. To achieve high effectiveness, we design a new technique to uncover the syntactics of CAN bus commands with backward slicing and dynamic forced execution, and a novel program-based algorithm to uncover the semantics of CAN bus commands by leveraging code-level semantics clues. We have implemented a prototype for both Android and iOS platforms, and tested it with all free car companion apps (253 in total) from both Google Play and Apple App Store. Among these apps, CANHUNTER discovered 182,619 syntactically unique CAN bus commands with 86% of them revealed with semantics, covering 360 car models from 21 car manufactures. We have also evaluated their correctness (both syntactics and semantics) using public resources, cross-platform and cross-app validation, and also real-car testing, in which 70% of all the uncovered commands are validated. We observe no inconsistency in cross-platform and cross-app validation, and only discover 3 false positives (among the 241 manually validated CAN bus commands) in semantics recovery from public resources and real-car testing.