| Title | Towards Reverse Engineering Controller Area Network Messages Using Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Young, Clinton, Svoboda, Jordan, Zambreno, Joseph |
| Conference Name | 2020 IEEE 6th World Forum on Internet of Things (WF-IoT) |
| Date Published | jun |
| Keywords | automotive security, classification, controller area network, controller area network security, Cyber-physical systems, Internet of Things, machine learning, pubcrawl, Resiliency, reverse engineering |
| 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. |
| DOI | 10.1109/WF-IoT48130.2020.9221383 |
| Citation Key | young_towards_2020 |
Towards Reverse Engineering Controller Area Network Messages Using Machine Learning