A Hybrid Machine Learning Model for Intrusion Detection in VANET

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This publication doesn't include Faculty of Medicine. It includes Faculty of Informatics. Official publication website can be found on muni.cz.

BANGUI Hind GE Mouzhi BÜHNOVÁ Barbora

Year of publication 2022
Type Article in Periodical
Magazine / Source Computing
MU Faculty or unit

Faculty of Informatics

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.
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