Campus Units

Electrical and Computer Engineering

Document Type

Conference Proceeding

Conference

2020 IEEE 6th World Forum on Internet of Things (WF-IoT)

Publication Version

Accepted Manuscript

Link to Published Version

https://doi.org/10.1109/WF-IoT48130.2020.9221383

Publication Date

10-13-2020

Journal or Book Title

2020 IEEE 6th World Forum on Internet of Things (WF-IoT)

DOI

10.1109/WF-IoT48130.2020.9221383

Conference Title

2020 IEEE 6th World Forum on Internet of Things (WF-IoT)

Conference Date

June 2-16, 2020

Abstract

The automotive Controller Area Network (CAN) allows Electronic Control Units (ECUs) to communicate with each other and control various vehicular functions such as engine and braking control. Consequently CAN and ECUs are high priority targets for hackers. As CAN implementation details are held as proprietary information by vehicle manufacturers, it can be challenging to decode and correlate CAN messages to specific vehicle operations. To understand the precise meanings of CAN messages, reverse engineering techniques that are time-consuming, manually intensive, and require a physical vehicle are typically used. This work aims to address the process of reverse engineering CAN messages for their functionality by creating a machine learning classifier that analyzes messages and determines their relationship to other messages and vehicular functions. Our work examines CAN traffic of different vehicles and standards to show that it can be applied to a wide arrangement of vehicles. The results show that the function of CAN messages can be determined without the need to manually reverse engineer a physical vehicle.

Comments

This is a manuscript of a proceeding published as Young, Clinton, Jordan Svoboda, and Joseph Zambreno. "Towards Reverse Engineering Controller Area Network Messages Using Machine Learning." In 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). (2020). DOI: 10.1109/WF-IoT48130.2020.9221383. Posted with permission.

Rights

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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