ReCAN – Dataset for reverse engineering of Controller Area Networks

• Main Authors: Mattia Zago, Stefano Longari, Andrea Tricarico, Michele Carminati, Manuel Gil Pérez, Gregorio Martínez Pérez, Stefano Zanero
• Format: Article
• Language: English
• Published: Elsevier 2020-04-01
• Series: Data in Brief
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352340920300433

This article details the methodology and the approach used to extract and decode the data obtained from the Controller Area Network (CAN) buses in two personal vehicles and three commercial trucks for a total of 36 million data frames. The dataset is composed of two complementary parts, namely the raw data and the decoded ones. Along with the description of the data, this article also reports both hardware and software requirements to first extract the data from the vehicles and secondly decode the binary data frames to obtain the actual sensors’ data. Finally, to enable analysis reproducibility and future researches, the code snippets that have been described in pseudo-code will be publicly available in a code repository. Motivated enough actors may intercept, interact, and recognize the vehicle data with consumer-grade technology, ultimately refuting, once-again, the security-through-obscurity paradigm used by the automotive manufacturer as a primary defensive countermeasure. Keywords: Automotive, Controller area network (CAN), Reverse engineering, Dataset