Aiden: Association-Learning-Based Attack Identification on the Edge of V2X Communication Networks
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
|Alam, M., Chen, Y. and Mumtaz, S.
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.
|Association Learning , Attack Identification , Automotive Security , Edge Intelligence , V2X Communication Networks
|IEEE Transactions on Green Communications and Networking
|Institute of Electrical and Electronics Engineers (IEEE)
|Digital Object Identifier (DOI)
|Web address (URL)
|05 Jul 2022
|Publication process dates
|22 Aug 2022
|Accepted author manuscript
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