@inproceedings{85dde40e9ba24cb09b207db3050a39d7,
title = "A Misbehavior Authority System for Sybil Attack Detection in C-ITS",
abstract = "Global misbehavior detection is an important backend mechanism in Cooperative Intelligent Transport Systems (C-ITS). It is based on the local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by Road-Side Units (RSUs) called Misbehavior Reports (MBRs) to the Misbehavior Authority (MA). By analyzing these reports, the MA provides more accurate and robust misbehavior detection results. Sybil attacks pose a significant threat to the C-ITS systems. Their detection and identification may be inaccurate and confusing. In this work, we propose a Machine Learning (ML) based solution for the internal detection process of the MA. We show through extensive simulation that our solution is able to precisely identify the type of the Sybil attack and provide promising detection accuracy results.",
keywords = "Cooperative Intelligent Transport Systems, Cyber-Security, Machine Learning, Misbehavior Detection, Sybil Attack",
author = "Joseph Kamel and Farah Haidar and Jemaa, \{Ines Ben\} and Arnaud Kaiser and Brigitte Lonc and Pascal Urien",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019 ; Conference date: 10-10-2019 Through 12-10-2019",
year = "2019",
month = oct,
day = "1",
doi = "10.1109/UEMCON47517.2019.8993045",
language = "English",
series = "2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1117--1123",
editor = "Satyajit Chakrabarti and Saha, \{Himadri Nath\}",
booktitle = "2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019",
}