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
Authors | Alam, M., Chen, Y. and Mumtaz, S. |
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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. |
Keywords | Association Learning , Attack Identification , Automotive Security , Edge Intelligence , V2X Communication Networks |
Year | 2022 |
Journal | IEEE Transactions on Green Communications and Networking |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
ISSN | 2473-2400 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TGCN.2022.3188674 |
Web address (URL) | https://ieeexplore.ieee.org/document/9815297 |
Publication dates | |
05 Jul 2022 | |
Publication process dates | |
Deposited | 22 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. |
https://openresearch.lsbu.ac.uk/item/918y4
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