: Controller Area Network (CAN) bus data is used on most vehicles today to report and communicate sensor data. However, this data is generally encoded and is not directly interpretable by simply viewing the raw data on the bus. However, it is possible to decode CAN bus data and reverse engineer the encodings by leveraging knowledge about how signals are encoded and using independently recorded ground-truth signal values for correlation. While methods exist to support the decoding of possible signals, these methods often require additional manual work to label the function of each signal. In this paper, we present CANClassify — a method that takes in raw CAN bus data, and automatically decodes and labels CAN bus signals, using a novel convolutional interpretation method to preprocess CAN messages. We evaluate CANClassify’s performance on a previously undecoded vehicle and confirm the encodings manually. We demonstrate performance comparable to the state of the art while also providing automated labeling. Examples and code are available at https://github.com/ngopaul/CANClassify .

CANClassify: Automated Decoding and Labeling of CAN Bus Signals

This study was motivated by the desire to provide highway managers/operators with more frequent and spatially dense information about the prevailing friction conditions in their networks. A new data-driven method was outlined for this purpose, wherein the prevailing tire–pavement grip potential is estimated from vehicle vibrations recorded during normal/regular usage of the infrastructure. The method was based on the underlying premise that transverse vehicle accelerations are related to wheel side-force oscillations, and therefore carry information related to the ride surface texture. It involved performing a short-time Fourier transform over vibration signals and analyzing the resulting spectral amplitudes. Two field experiments were carried out to validate the method. The first provided evidence of a statistical link between transverse vehicle vibrations and wheel side-force oscillations. The second tested the statistical link between skid resistance measured over a 26 km highway section and corresponding skid resistance estimations based on vehicle vibration data. Overall, transverse vehicle vibration characteristics were found to hold relevant information about the prevailing tire–pavement grip potential; the two were moderately inter-correlated. The newly proposed estimation method seems promising and potentially useful for pavement management applications, especially when considering the emergence of connected car technologies and the increased availability (and affordability) of in-vehicle Internet of Things devices.

Estimating the Tire–Pavement Grip Potential From Vehicle Vibrations

https://doi.org/10.1016/j.xinn.2023.100386

SparkLink: A short-range wireless communication protocol with ultra-low latency and ultra-high reliability

Modern connected vehicles are equipped with a large number of sensors, which enable a wide range of services that can improve overall traffic safety and efficiency. However, remote access to connected vehicles also introduces new security issues affecting both inter and intra-vehicle communications. In fact, existing intra-vehicle communication systems, such as Controller Area Network (CAN), lack security features, such as encryption and secure authentication for Electronic Control Units (ECUs). Instead, Original Equipment Manufacturers (OEMs) seek security through obscurity by keeping secret the proprietary format with which they encode the information. Recently, it has been shown that the reuse of CAN frame IDs can be exploited to perform CAN bus reverse engineering without physical access to the vehicle, thus raising further security concerns in a connected environment. This work investigates whether anonymizing the frames of each newly released vehicle is sufficient to prevent CAN bus reverse engineering based on frame ID matching. The results show that, by adopting Machine Learning techniques, anonymized CAN frames can still be fingerprinted and identified in an unknown vehicle with an accuracy of up to 80 %.

https://orbilu.uni.lu/bitstream/10993/48391/1/paper_stamped.pdf

On Frame Fingerprinting and Controller Area Networks Security in Connected Vehicles

As modern vehicles become more exposed to the outside world, many efforts have been made to enhance the security of in-vehicle Controller Area Network(CAN). These efforts are facing significant challenges, because the CAN signals that store the driving status of vehicle are encoded into specific format, thus preventing researchers from understanding its meaning. Unfortunately, the Database CAN(DBC) file that contain these encoding patterns are kept strictly confidential by the car manufacturer. Currently, many work has been conducted to reverse engineer CAN signals. However, through our experiments, these studies have shown highly inaccuracy in identifying the boundary of CAN signals. In this paper, we propose ACRE, an advanced CAN signal reverse engineering scheme based on a novel metric of adjacent-bit changing rate (ACR). With ACR, ACRE automatically segments CAN data field. Inspired by ACR, we further present Diagonal-bit Changing Rate (DCR) to determine the endianness of CAN signal, thus accomplishing signal extraction and signedness discrimination. Verified by real-world dataset and corresponding DBC, ACRE achieves 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> coverage, and its vehicle-series compatibility have been proven by extensive experiments on 4 datasets of real car. 

ACRE: An Advanced CAN Signal Reverse Engineering Scheme

/pdf/canmatch-a-fully-automated-tool-for-can-bus-reverse-jlyi7rmqnp.pdf

Romain Crunelle

Papers

CANMatch: A Fully Automated Tool for CAN Bus Reverse Engineering based on Frame Matching