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Title	Sybil Attack Detection In Vanets Using An Adaboost Classifier
ID_Doc	54168
Authors	Laouiti D.E.; Ayaida M.; Messai N.; Najeh S.; Najjar L.; Chaabane F.
Year	2022
Published	2022 International Wireless Communications and Mobile Computing, IWCMC 2022
DOI	http://dx.doi.org/10.1109/IWCMC55113.2022.9824974
Abstract	Smart cities are a wide range of projects made to facilitate the problems of everyday life and ensure security. Our interest focuses only on the Intelligent Transport System (ITS) that takes care of the transportation issues using the Vehicular Ad-Hoc Network (VANET) paradigm as its base. VANETs are a promising technology for autonomous driving that provides many benefits to the user conveniences to improve road safety and driving comfort. VANET is a promising technology for autonomous driving that provides many benefits to the user's conveniences by improving road safety and driving comfort. The problem with such rapid development is the continuously increasing digital threats. Among all these threats, we will target the Sybil attack since it has been proved to be one of the most dangerous attacks in VANETs. It allows the attacker to generate multiple forged identities to disseminate numerous false messages, disrupt safety-related services, or misuse the systems. In addition, Machine Learning (ML) is showing a significant influence on classification problems, thus we propose a behavior-based classification algorithm that is tested on the provided VeReMi dataset coupled with various machine learning techniques for comparison. The simulation results prove the ability of our proposed mechanism to detect the Sybil attack in VANETs. © 2022 IEEE.
Author Keywords	Machine learning; Misbehavior detection; Sybil attack; VANET Security; Vehicle Driving Pattern

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