Underlying Data for 'Detection of False Position Attacks in VANETs through Bagging Ensemble Learning'

This dataset comprises curated subsets of the VeReMi (Vehicular Misbehavior Detection) dataset, specifically extracted and preprocessed to facilitate the detection of False Position Attacks (FPAs) in Vehicular Ad-hoc Networks (VANETs). The data preparation was conducted as part of the research presented in the manuscript “Detection of False Position Attacks in VANETs through Bagging Ensemble Learning”, submitted to PLOS ONE (Manuscript ID: PONE-D-25-17735).The original VeReMi dataset provides synthetic vehicular communication traces that simulate both benign and malicious behaviors in VANET scenarios. From this, we selected five specific attack types and reformatted the data into a machine learning–friendly structure to support the supervised classification of FPAs.Original Source:The original VeReMi dataset is publicly available at: https://arxiv.org/abs/1804.06701(Cite as: Van der Heijden, R. W., et al., "VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs", arXiv preprint arXiv:1804.06701, 2018.)Included Files:The dataset includes the following five CSV files, each corresponding to a different simulated false position attack scenario:at1.csv — Attack Type 1at2.csv — Attack Type 2at4.csv — Attack Type 4at8.csv — Attack Type 8at16.csv — Attack Type 16Feature Description:Each row represents a single instance of vehicular behavior, including the following features:pos-x1, pos-y1: Position coordinates of vehicle 1spd-x1, spd-y1: Velocity components of vehicle 1pos-x2, pos-y2: Position coordinates of vehicle 2spd-x2, spd-y2: Velocity components of vehicle 2sendtime_1, sendtime_2: Message send timestamps, used to derive the feature time_intervalAttackerType: Class label indicating the type of attacker (used as the target variable for classification)Use Case and Analysis:The dataset was used to evaluate the effectiveness of multiple machine learning models in detecting FPAs. Specifically, the following classifiers were tested:Decision Tree (CART)Random Forest (RF)K-Nearest Neighbors (KNN)Multilayer Perceptron (MLP)Each model was assessed both with and without bagging to examine the impact of ensemble learning on classification performance. Results from our study demonstrate that KNN enhanced with bagging consistently outperforms other configurations across all attack types.Reuse Potential:This dataset is intended for researchers and practitioners in the fields of vehicular network security, misbehavior detection, and machine learning, particularly those exploring ensemble methods for anomaly detection. It enables reproducibility of our results and provides a foundation for benchmarking alternative detection techniques.