CANClassify: Automated Decoding and Labeling of CAN Bus Signals
by Paul Ngo 1,* 
, Jonathan Sprinkle 2 , Rahul Bhadani 3
1 University of California, Berkeley, Berkeley, California, USA, 94704
2 Vanderbilt University, Nashville, Tennessee, USA, 37212
3 The University of Arizona, Tucson, Arizona, USA, 85721
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 10, Page # 5-12, 2022; DOI: 10.55708/js0110002
Keywords: External interfaces for robotics, Computing methodologies, Learning paradigms, Neural
networks
Received: 19 July 2022, Revised: 20 September 2022, Accepted: 21 September 2022, Published Online: 10 October 2022
APA Style
Ngo, P., Sprinkle, J., & Bhadani, R. (2022). CANClassify: Automated Decoding and Labeling of CAN Bus Signals. Journal of Engineering Research and Sciences, 1(10), 5–12. https://doi.org/10.55708/js0110002
Chicago/Turabian Style
Ngo, Paul, Jonathan Sprinkle, and Rahul Bhadani. “CANClassify: Automated Decoding and Labeling of CAN Bus Signals.” Journal of Engineering Research and Sciences 1, no. 10 (October 1, 2022): 5–12. https://doi.org/10.55708/js0110002.
IEEE Style
P. Ngo, J. Sprinkle, and R. Bhadani, “CANClassify: Automated Decoding and Labeling of CAN Bus Signals,” Journal of Engineering Research and Sciences, vol. 1, no. 10, pp. 5–12, Oct. 2022, doi: 10.55708/js0110002.
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.
- M. Nice, S. Elmadani, R. Bhadani, M. Bunting, J. Sprinkle, D. Work, CAN Coach: Vehicular Control through Human Cyber-Physical Systems, p. 132–142, Association for Computing Machinery, New York, NY, USA, 2021.
- E. Baschab, S. Ball, A. Vazzana, J. Sprinkle, Safer Adaptive Cruise Control for Traffic Wave Dampening, p. 231–232, Association for Computing Machinery, New York, NY, USA, 2021.
- S. Jafarnejad, “Machine learning-based methods for driver identifica- tion and behavior assessment: Applications for can and floating car data”, 2020.
- M. Bertoncello, “Monetizing car data”, 2017.
- “Sae j1939 standards collection”, 1994.
- Comma.ai, “opendbc: The project to democratize access to the de-
- J. de Hoog, T. Bogaerts, W. Casteels, S. Mercelis, P. Hellinckx, “Online reverse engineering of can data”, Internet of Things, vol. 11, p. 100232, 2020, doi:https://doi.org/10.1016/j.iot.2020.100232.
- M. E. Verma, R. A. Bridges, J. J. Sosnowski, S. C. Hollifield, M. D. Iannacone, “CAN-D: A Modular Four-Step Pipeline for Compre- hensively Decoding Controller Area Network Data”, arXiv e-prints, arXiv:2006.05993, 2020.
- S. Siami-Namini, N. Tavakoli, A. Siami Namin, “A comparison of arima and lstm in forecasting time series”, “2018 17th IEEE Interna- tional Conference on Machine Learning and Applications (ICMLA)”, pp. 1394–1401, 2018, doi:10.1109/ICMLA.2018.00227.
- F. Karim, S. Majumdar, H. Darabi, S. Chen, “Lstm fully convolu- tional networks for time series classification”, IEEE Access, vol. 6, pp. 1662–1669, 2018, doi:10.1109/ACCESS.2017.2779939.
- R. Pascanu, C. Gulcehre, K. Cho, Y. Bengio, “How to construct deep recurrent neural networks”, 2014.
- M. Marchetti, D. Stabili, “Read: Reverse engineering of automotive data frames”, IEEE Transactions on Information Forensics and Security, vol. 14, no. 4, pp. 1083–1097, 2019, doi:10.1109/TIFS.2018.2870826.
- M. D. Pesé, T. Stacer, C. A. Campos, E. Newberry, D. Chen, K. G. Shin, “Librecan: Automated can message translator”, “Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security”, CCS ’19, p. 2283–2300, Association for Computing Machinery, New York, NY, USA, 2019, doi:10.1145/3319535.3363190.
- M. E. Verma, R. A. Bridges, J. J. Sosnowski, S. C. Hollifield,
M. D. Iannacone, “Can-d: A modular four-step pipeline for comprehensively decoding controller area network data”, 2020, doi: 10.48550/ARXIV.2006.05993. - A. Buscemi, I. Turcanu, G. Castignani, R. Crunelle, T. Engel, “Can- match: A fully automated tool for can bus reverse engineering based on frame matching”, IEEE Transactions on Vehicular Technology, vol. 70, no. 12, pp. 12358–12373, 2021, doi:10.1109/TVT.2021.3124550.
- M. Bunting, R. Bhadani, J. Sprinkle, “Libpanda: A high performance library for vehicle data collection”, “Proceedings of the Workshop on Data-Driven and Intelligent Cyber-Physical Systems”, DI-CPS’21, p. 32–40, Association for Computing Machinery, New York, NY, USA, 2021, doi:10.1145/3459609.3460529.
- K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”, Biological Cybernetics, vol. 36, no. 4, pp. 193–202, 1980, doi:10.1007/BF00344251.
- R. Bhadani, M. Bunting, M. Nice, N. M. Tran, S. Elmadani, D. Work, J. Sprinkle, “Strym: A python package for real-time can data log- ging, analysis and visualization to work with usb-can interface”, “Proceedings of the 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems”, 2022.
- J. Kleylein-Feuerstein, F. Joas, R. Steinhilper, “Remanufacturing of electronic control units: An rfid based (service) interface”, Procedia CIRP, vol. 29, pp. 168–172, 2015, doi:https://doi.org/10.1016/j.procir. 2015.02.163, the 22nd CIRP Conference on Life Cycle Engineering.
- M. Markovitz, A. Wool, “Field classification, modeling and anomaly ion in unknown can bus networks”, Veh. Commun., vol. 9, pp.–52, 2017.
- M. H. Shahriar, Y. Xiao, P. Moriano, W. Lou, Y. T. Hou, “Canshield: Signal-based intrusion detection for controller area networks”, 2022.
- O. Avatefipour, H. Malik, “State-of-the-art survey on in-vehicle net- ation (can-bus) security and vulnerabilities”, 2018,
0.48550/ARXIV.1802.01725.
