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• W4205656656 abstract "Vehicular ad-hoc network (VANET) is an emerging technology for vehicle-to-vehicle communication vital for reducing road accidents and traffic congestion in an Intelligent Transportation System (ITS). VANET communication is vulnerable to various attacks and cryptographic techniques are commonly used for message integrity and authentication of vehicles. However, cryptograhpic techniques alone may not be sufficient to protect against insider attacks. Many VANET safety applications rely on periodic broadcast of basic safety messages (BSMs) from surrounding vehicles that contain important status information about a vehicle such as its position, speed, and heading. If an attacker (misbehaving vehicle) injects false position information in a BSM, it can lead to serious consequences including traffic congestion or even accidents. Therefore, it is imperative to accurately detect and identify such attackers to ensure safety in the network. This paper presents a novel data-centric approach to detect <i>position falsification</i> attacks, using machine learning (ML) algorithms. Unlike existing techniques, the proposed approach combines information from 2 consecutive BSMs for training and testing. Simulations using the Vehicular Reference Misbehavior (VeReMi) dataset demonstrate that the proposed model clearly outperforms existing approaches for identifying a range of different attack types." @default.
• W4205656656 created "2022-01-25" @default.
• W4205656656 creator A5055035963 @default.
• W4205656656 creator A5061543036 @default.
• W4205656656 date "2022-01-01" @default.
• W4205656656 modified "2023-09-26" @default.
• W4205656656 title "Machine Learning Based Misbehaviour Detection in VANET Using Consecutive BSM Approach" @default.
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• W4205656656 doi "https://doi.org/10.1109/ojvt.2021.3138354" @default.
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