@inproceedings{b625ca7e34ba416d8b2693c41805ccba,
title = "Misbehavior Detection in C-ITS: A comparative approach of local detection mechanisms",
abstract = "MisBehavior Detection (MBD) is an important security mechanism in Cooperative Intelligent Transport Systems (C-ITS). It involves monitoring C-ITS communications to detect potentially misbehaving entities. This monitoring is based on local plausibility and consistency checks done by the Intelligent Transport Systems (ITS) Station (ITS-S) on every received Vehicle-to-Everything (V2X) message. These checks are then analyzed by a local detection mechanisms to estimate the overall plausibility of a message. In this paper we focus on the logic behind different local detection mechanisms. First, we propose different local detection solutions based on logics extracted from the state of the art. Then we present a comparative review of the detection quality and the computation latency of each proposed mechanisms.",
keywords = "C-ITS, Machine Learning, Misbehavior Detection",
author = "Joseph Kamel and Jemaa, \{Ines Ben\} and Arnaud Kaiser and Loic Cantat and Pascal Urien",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Vehicular Networking Conference, VNC 2019 ; Conference date: 04-12-2019 Through 06-12-2019",
year = "2019",
month = dec,
day = "1",
doi = "10.1109/VNC48660.2019.9062831",
language = "English",
series = "IEEE Vehicular Networking Conference, VNC",
publisher = "IEEE Computer Society",
editor = "Danijela Cabric and Onur Altintas and Tim Leinmueller and Hongwei Zhang and Takamasa Higuchi",
booktitle = "2019 IEEE Vehicular Networking Conference, VNC 2019",
}