Aiden: Association-Learning-Based Attack Identification on the Edge of V2X Communication Networks

Journal article


Alam, M., Chen, Y. and Mumtaz, S. (2022). Aiden: Association-Learning-Based Attack Identification on the Edge of V2X Communication Networks. IEEE Transactions on Green Communications and Networking. https://doi.org/10.1109/TGCN.2022.3188674
AuthorsAlam, M., Chen, Y. and Mumtaz, S.
Abstract

In vehicle security, attack identification has been proposed to identify the compromised electronic control units (ECUs) of a vehicle. Fingerprinting methods using a variety of features have been widely applied to identify attacks. However, these methods only consider the features of an individual ECU, and ignore the logical association among different ECUs. This condition leads to high requirements in terms of feature measurements, and a great deal of useful information is lost to achieve identification. In this paper, an association-learning-based model, designated Aiden, is proposed to identify the compromised ECUs on the edge of V2X communication networks and without feature measurements. Experiments on a real vehicle show the effectiveness of the proposed model.

KeywordsAssociation Learning , Attack Identification , Automotive Security , Edge Intelligence , V2X Communication Networks
Year2022
Journal IEEE Transactions on Green Communications and Networking
PublisherIEEE
ISSN2473-2400
Digital Object Identifier (DOI)https://doi.org/10.1109/TGCN.2022.3188674
Web address (URL)https://ieeexplore.ieee.org/document/9815297
Publication dates
Print05 Jul 2022
Publication process dates
Deposited22 Aug 2022
Accepted author manuscript
License
File Access Level
Open
Additional information

© 2022 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.

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Accepted author manuscript
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License: CC BY 4.0
File access level: Open

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