TY - GEN
T1 - VeReMi Extension
T2 - 2020 IEEE International Conference on Communications, ICC 2020
AU - Kamel, Joseph
AU - Wolf, Michael
AU - Van Der Hei, Rens W.
AU - Kaiser, Arnaud
AU - Urien, Pascal
AU - Kargl, Frank
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - Dataset
KW - Intelligent Transport Systems
KW - Misbehavior Detection
KW - Vehicular Networks
U2 - 10.1109/ICC40277.2020.9149132
DO - 10.1109/ICC40277.2020.9149132
M3 - Conference contribution
AN - SCOPUS:85089410353
T3 - IEEE International Conference on Communications
BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 June 2020 through 11 June 2020
ER -
