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- W4381744978 abstract "Connected and Autonomous Vehicles (CAVs) will play a critical role in improving the safety and efficiency of future Intelligent Transportation System (ITS). Periodic broadcasts of basic safety messages (BSMs) containing up-to-date vehicle status information constitute one important class of inter-vehicular communication. If false or inaccurate information is inserted in the BSMs it can lead to serious consequences such as accidents resulting in bodily injury and even loss of life. In this paper, we propose a machine learning based approach for automatically detecting replay attacks, where BSMs received from neighboring vehicles are rebroadcast, with false sender information, by a malicious node. Simulations using the publicly available Vehicular Reference Misbehavior (VeReMi) extension dataset demonstrate that the proposed model clearly outperforms existing techniques for detecting BSM replay attacks." @default.
- W4381744978 created "2023-06-24" @default.
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- W4381744978 date "2023-05-08" @default.
- W4381744978 modified "2023-09-26" @default.
- W4381744978 title "Machine learning based detection of replay attacks in VANET" @default.
- W4381744978 doi "https://doi.org/10.1109/noms56928.2023.10154299" @default.
- W4381744978 hasPublicationYear "2023" @default.
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