Towards a Reliable Machine Learning Based Global Misbehavior Detection in C-ITS: Model Evaluation Approach - Archive ouverte HAL Access content directly
Conference Papers Year :

Towards a Reliable Machine Learning Based Global Misbehavior Detection in C-ITS: Model Evaluation Approach

(1) , (1, 2) , (1) , (1) , (3)
1
2
3
Issam Mahmoudi
  • Function : Author
  • PersonId : 1057801
Joseph Kamel
  • Function : Author
  • PersonId : 1031548
Ines Ben-Jemaa
  • Function : Author
  • PersonId : 1057802
Arnaud Kaiser
  • Function : Author
Pascal Urien

Abstract

Global misbehavior detection in Cooperative Intelligent Transport Systems (C-ITS) is carried out by a central entity named Misbe-havior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by RoadSide Units (RSUs) called Misbehavior Reports (MBRs) to the MA. By analyzing these Misbehavior Reports (MBRs), the MA is able to compute various misbehavior detection information. In this work, we propose and evaluate different Machine Learning (ML) based solutions for the internal detection process of the MA. We show through extensive simulation and several detection metrics the ability of solutions to precisely identify different misbehavior types.
Fichier principal
Vignette du fichier
MA-CameraReady.pdf (723.57 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02353893 , version 1 (07-11-2019)

Identifiers

  • HAL Id : hal-02353893 , version 1

Cite

Issam Mahmoudi, Joseph Kamel, Ines Ben-Jemaa, Arnaud Kaiser, Pascal Urien. Towards a Reliable Machine Learning Based Global Misbehavior Detection in C-ITS: Model Evaluation Approach. International Workshop on Vehicular Adhoc Networks for Smart Cities (IWVSC'2019), Nov 2019, Paris, France. ⟨hal-02353893⟩
1164 View
423 Download

Share

Gmail Facebook Twitter LinkedIn More