Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning

Uchenna Ezeobi

doi:10.1109/MCE.2020.3023538

Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning

2022-01-01

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DOI：10.1109/MCE.2020.3023538

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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 hundreds 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.

Keywords:

Smart cities

Reverse engineering

Security

Machine learning

Ecosystems

Clustering algorithms

Complexity theory

Reverse Engineering Controller Area Network Messages Using Unsupervised Machine Learning

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