ACTT: Automotive CAN tokenization and translation

Miki Verma; Robert Bridges; Samuel Hollifield

doi:10.1109/CSCI46756.2018.00061

ACTT: Automotive CAN tokenization and translation

Miki Verma, Robert Bridges, Samuel Hollifield

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

23 Scopus citations

Abstract

Modern vehicles contain scores of Electrical Control Units (ECUs) that broadcast messages over a Controller Area Network (CAN). Vehicle manufacturers rely on security through obscurity by concealing their unique mapping of CAN messages to vehicle functions which differs for each make, model, year, and even trim. This poses a major obstacle for after-market modifications notably performance tuning and in-vehicle network security measures. We present ACTT: Automotive CAN Tokenization and Translation, a novel, vehicle-agnostic, algorithm that leverages available diagnostic information to parse CAN data into meaningful messages, simultaneously cutting binary messages into tokens, and learning the translation to map these contiguous bits to the value of the vehicle function communicated.

Original language	English
Title of host publication	Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	278-283
Number of pages	6
ISBN (Electronic)	9781728113609
DOIs	https://doi.org/10.1109/CSCI46756.2018.00061
State	Published - Dec 2018
Event	2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018 - Las Vegas, United States Duration: Dec 13 2018 → Dec 15 2018

Publication series

Name	Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018

Conference

Conference	2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
Country/Territory	United States
City	Las Vegas
Period	12/13/18 → 12/15/18

Funding

ACKNOWLEDGEMENTS Special thanks to Micheal Iannacone and anonymous reviewers. Research sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy (DOE) and by the DOE, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Scientific Undergraduate Laboratory Internship (SULI) program.

Keywords

Controller area network (CAN)
DBC signal
Regression
Reverse engineering
Tokenization

Access to Document

10.1109/CSCI46756.2018.00061

Cite this

Verma, M., Bridges, R., & Hollifield, S. (2018). ACTT: Automotive CAN tokenization and translation. In Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018 (pp. 278-283). Article 8947694 (Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI46756.2018.00061

Verma, Miki ; Bridges, Robert ; Hollifield, Samuel. / ACTT : Automotive CAN tokenization and translation. Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 278-283 (Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018).

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abstract = "Modern vehicles contain scores of Electrical Control Units (ECUs) that broadcast messages over a Controller Area Network (CAN). Vehicle manufacturers rely on security through obscurity by concealing their unique mapping of CAN messages to vehicle functions which differs for each make, model, year, and even trim. This poses a major obstacle for after-market modifications notably performance tuning and in-vehicle network security measures. We present ACTT: Automotive CAN Tokenization and Translation, a novel, vehicle-agnostic, algorithm that leverages available diagnostic information to parse CAN data into meaningful messages, simultaneously cutting binary messages into tokens, and learning the translation to map these contiguous bits to the value of the vehicle function communicated.",

keywords = "Controller area network (CAN), DBC signal, Regression, Reverse engineering, Tokenization",

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note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018 ; Conference date: 13-12-2018 Through 15-12-2018",

year = "2018",

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Verma, M, Bridges, R & Hollifield, S 2018, ACTT: Automotive CAN tokenization and translation. in Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018., 8947694, Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 278-283, 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, Las Vegas, United States, 12/13/18. https://doi.org/10.1109/CSCI46756.2018.00061

ACTT: Automotive CAN tokenization and translation. / Verma, Miki; Bridges, Robert; Hollifield, Samuel.
Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 278-283 8947694 (Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Verma M, Bridges R, Hollifield S. ACTT: Automotive CAN tokenization and translation. In Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 278-283. 8947694. (Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018). doi: 10.1109/CSCI46756.2018.00061

ACTT: Automotive CAN tokenization and translation

Abstract

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