ACTT: Automotive CAN Tokenization and Translation

Verma, Miki E.; Bridges, Robert A.; Hollifield, Samuel C.

doi:10.1109/csci46756.2018.00061

Cited by 22 publications

(26 citation statements)

References 15 publications

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“…READ [6] is a novel algorithm that isolates counters and CRCs among other values to label signal types based on data frames in CAN traces. ACTT [7] leverages diagnostic information to parse CAN by breaking messages into tokens and then learning the translation from bits to vehicle function. Li-breCAN [8] captures the bit-flip rate of messages and uses them along with sensor data from a smartphone to classify messages.…”

Section: Background and Related Workmentioning

confidence: 99%

Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning

Ezeobi

Olufowobi

Young

et al. 2022

IEEE Consumer Electron. Mag.

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The smart city landscape is rife with opportunities for mobility and economic optimization, but also presents many security concerns spanning the range of components and systems in the smart ecosystem. One key enabler for this ecosystem is smart transportation and transit, which is foundationally built upon connected vehicles. Ensuring vehicular security, while necessary to guarantee passenger and pedestrian safety, is itself challenging due to the broad attack surfaces of modern automotive systems. A single car contains dozens to hundreds of small embedded computing devices known as electronic control units (ECUs) executing 100s of millions of lines of code; the inherent complexity of this tightly-integrated cyber-physical system (CPS) is one of the key problems that frustrate effective security. We describe an approach to help reduce the complexity of security analyses by leveraging unsupervised machine learning to learn clusters of messages passed between ECUs that correlate with changes in the CPS state of a vehicle as it moves throughout the world. Our approach can help to improve the security of vehicles in a smart city, and can leverage smart city infrastructure to further enrich and refine the quality of the machine learning output.

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Section: Background and Related Workmentioning

confidence: 99%

Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning

Ezeobi

Olufowobi

Young

et al. 2022

IEEE Consumer Electron. Mag.

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“…Their method isolates counters and cyclic redundancy checks (CRCs), among other values, to label the signals. Verma et al [12], proposed a simple algorithm to extract CAN message signals and label them using OBD-II PIDs. Their algorithm, ACTT: Automotive CAN Tokenization and Translation, leverages diagnostic information to parse CAN by breaking messages into tokens and then learning the translation from bits to vehicle function.…”

Section: Related Workmentioning

confidence: 99%

Towards Reverse Engineering Controller Area Network Messages Using Machine Learning

Young

Svoboda

Zambreno

2020

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

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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.

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“…Verma et al [7] introduce ACTT, which tokenizes and translates the signals matching the information obtained by injecting diagnostic messages through the OBD-II port. Like READ [6], ACTT identifies only the signals whose bits flip during the data collection.…”

Section: Background and Related Workmentioning

confidence: 99%

A Data-Driven Minimal Approach for CAN Bus Reverse Engineering

Buscemi

Castignani

Engel

et al. 2020

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)

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Current in-vehicle communication systems lack security features, such as encryption and secure authentication. The approach most commonly used by car manufacturers is to achieve security through obscurity-keep the proprietary format used to encode the information secret. However, it is still possible to decode this information via reverse engineering. Existing reverse engineering methods typically require physical access to the vehicle and are time consuming. In this paper, we present a Machine Learning-based method that performs automated Controller Area Network (CAN) bus reverse engineering while requiring minimal time, hardware equipment, and potentially no physical access to the vehicle. Our results demonstrate high accuracy in identifying critical vehicle functions just from analysing raw traces of CAN data.

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ACTT: Automotive CAN Tokenization and Translation

Cited by 22 publications

References 15 publications

Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning

Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning

Towards Reverse Engineering Controller Area Network Messages Using Machine Learning

A Data-Driven Minimal Approach for CAN Bus Reverse Engineering

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