ACTT: Automotive CAN tokenization and translation

Miki Verma, Robert Bridges, Samuel Hollifield

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 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 languageEnglish
Title of host publicationProceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages278-283
Number of pages6
ISBN (Electronic)9781728113609
DOIs
StatePublished - Dec 2018
Event2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018 - Las Vegas, United States
Duration: Dec 13 2018Dec 15 2018

Publication series

NameProceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018

Conference

Conference2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
Country/TerritoryUnited States
CityLas Vegas
Period12/13/1812/15/18

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.

FundersFunder number
Office of Workforce Development for Teachers
U.S. Department of Energy
Office of Science
Oak Ridge National Laboratory

    Keywords

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

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