A Hybrid Machine Learning Model for Intrusion Detection in VANET

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Authors	BANGUI Hind GE Mouzhi BÜHNOVÁ Barbora
Year of publication	2022
Type	Article in Periodical
Magazine / Source	Computing
MU Faculty or unit	Faculty of Informatics
Citation
Web	https://doi.org/10.1007/s00607-021-01001-0
Doi	http://dx.doi.org/10.1007/s00607-021-01001-0
Keywords	Machine learning; VANET; Security; Intrusion; Clustering; Classification; Coresets; Random Forest
Description	While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attacks in VANET. However, dealing with a large amount of vehicular data that keep growing in the urban environment is still a critical challenge for IDSs. This paper, therefore, proposes a new machine learning model to improve the performance of IDSs by using Random Forest and a posterior detection based on coresets to improve the detection accuracy and increase detection efficiency. The experimental results show that the proposed machine learning model can significantly enhance the detection accuracy compared to classical application of machine learning models.
Related projects:	CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence