VeReMi_Extension: Dataset for Misbehaviors in VANETs
Description
This VeReMi Extension dataset consists of a set of misbehaviors in Vehicular Ad hoc Networks (VANETs) • Original dataset: The dataset used in this study is based on an existing dataset that Kamel et al. [1] published in 2019. They made use of the vehicle trace data from the Luxembourg SUMO Traffic (LuST) scenario, an open-source synthetic traffic scenario verified using real data from the VehicularLab of the University of Luxembourg. 39 resulting datasets make up this dataset. • Prepared dataset: We combined all 39 datasets into a single CSV file for our dataset. Ground-truth data and log files are included with each simulation. There is only one ground truth file that describes how a vehicle actually acts in the network, and it is used to run simulations. The ground truth file also includes an attacker type to distinguish between legitimate and misbehaving vehicles. However, in a simulation, the number of log files is equal to the number of vehicles on the network. Every vehicle creates a log file that contains all of the BSMs that were received. The first step is to combine all of the various log files into one file because there are as many log files as there are receivers. The ground truth file must then be mapped to the log files for each simulation in order to link the log files and ground truth files. We added a categorical feature called "class" for the target class to the combined dataset in order to create a labeled database. This dataset has an uneven class structure because the VeReMi Extension comprises 3,194,808 instances, with the normal class making up 59,488% of the whole database. The publication of this work on misbehavior detection is in [2] and [3]. Researchers in a variety of disciplines, including artificial intelligence and misbehavior detection in VANETs, can use our labeled dataset. When utilizing this dataset, please refer to the respective papers [2] and [3]. [1] J. Kamel, “Github repository: Framework for misbehavior detection (f2md),” 2019. [Online]. Available: https://github.com/josephkamel/f2md [2] O. Slama, B. Alaya, and S. Zidi, “Towards Misbehavior Intelligent Detection Using Guided Machine Learning in Vehicular Ad-hoc Networks (VANET),” Inteligencia Artificial, vol. 25, no. 70, pp. 138–154, 2022, doi: 10.4114/intartif.vol25iss70pp138-154. [3] O. Slama, B. Alaya, S. Zidi, and M. Tarhouni, “Comparative Study of Misbehavior Detection System for Classifying misbehaviors on VANET.,” in 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, May 2022, vol. 1, pp. 243–248.