Campus Units

Electrical and Computer Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2020

Journal or Book Title

IEEE Consumer Electronics Magazine

DOI

10.1109/MCE.2020.3023538

Abstract

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.

Comments

This is a manuscript of an article published as Ezeobi, Uchenna, Habeeb Olufowobi, Clinton Young, Joseph Zambreno, and Gedare Bloom. "Reverse Engineering Controller Area Network Messages using Unsupervised Machine Learning." IEEE Consumer Electronics Magazine (2020). DOI: 10.1109/MCE.2020.3023538. 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

Share

COinS