VeReMi Extension: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs

Abstract

Cooperative Intelligent Transport Systems (C-ITS) is a new upcoming technology that aims at increasing road safety and reducing traffic accidents. C-ITS is based on peer-to-peer messages sent on the Vehicular Ad hoc NETwork (VANET). VANET messages are currently authenticated using digital keys from valid certificates. However, the authenticity of a message is not a guarantee of its correctness. Consequently, a misbehavior detection system is needed to ensure the correct use of the system by the certified vehicles. Although a large number of studies are aimed at solving this problem, the results of these studies are still difficult to compare, reproduce and validate. This is due to the lack of a common reference dataset. For this reason, the original VeReMi dataset was created. It is the first public misbehavior detection dataset allowing anyone to reproduce and compare different results. VeReMi is used in a number of studies and is currently the only dataset in its field. In this Paper, we extend the dataset by adding realistic a sensor error model, a new set of attacks and larger number of data points. Finally, we also provide benchmark detection metrics using a set of local detectors and a simple misbehavior detection mechanism.

An Improved Robust Misbehavior Detection Scheme for Vehicular Ad Hoc Network

“…𝑓 ̂𝑘 − 𝑓 𝑘|𝑘−1 → 𝑧 𝑘 (11) This information can be used to achieve two objectives. The first is improving the accuracy, and the second is ensuring security.…”

“…The performance of VANET applications depends on the quality of the integrity of the cooperative awareness messages broadcasted by the nearby vehicles [2,[7][8][9]. However, misbehaving vehicles which broadcast inaccurate information can disrupt the fundamental operations of VANET applications [10,11]. Thus, security is essential to release VANET applications' potential to improve road safety and traffic efficiency and provide passenger comfort [12,13].…”

“…Many misbehaviors detection have been proposed for VANET in the literature [7,9,[11][12][13][14][15][16][17][18][19][20]. Most of these solutions are based on data-centric that classify the vehicles based on the quality of the cooperative awareness messages shared among neighboring vehicles.…”

RTED-SD: A Real-Time Edge Detection Scheme for Sybil DDoS in the Internet of Vehicles

“…VeReMi is used in a number of studies and is currently the only dataset in its field. On this basis, J. Kamel extended the dataset [36] by adding realistic a sensor error model, a new set of attacks and larger number of data points. We work on the DosRandomSybil datasets [37].…”

CT2‐MDS: Cooperative trust‐aware tolerant misbehaviour detection system for connected and automated vehicles

“…Different algorithms' performances are compared, and the irrationality of the label in the dataset is observed. Joseph et al [31] also improve the VeReMi from attack type, attack density, and vehicle message. However, due to the traffic scene's ideal setting, the VeReMi dataset does not comply well with the highly mobile nature of V2X, and the existing misbehaviour detection solutions do not consider the inherent collection errors of vehicular sensors.…”