A Comparative Review of Security Threats Datasets for Vehicular Networks
Dorsaf Swessi, Hanen Idoudi
Abstract
With the rapid growth of vehicular technology, Vehicle-to-everything (V2X) communication systems are becoming increasingly challenging, especially regarding security aspects. Using Machine Learning (ML) techniques to build Intrusion Detection Systems (IDS) has shown a high level of accuracy in minimizing V2X communications attacks. However, the effectiveness of ML-based IDSs depends on the availability of a sufficient amount of relevant network traffic logs that cover a wide variety of normal and abnormal samples to train and verify these models. In this paper, we provide the most up-to-date review of existing V2X security datasets. We classify these datasets according to the targeted architecture, the involved attacks, and their severity, etc. Based on these different effectiveness criteria we suggest four distinct yet realistic and reliable datasets including ROAD, VDDD, VeReMi, and VDOS-LRS datasets.