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Communication Dans Un Congrès Année : 2019

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

Issam Mahmoudi
  • Fonction : Auteur
  • PersonId : 1057801
Joseph Kamel
  • Fonction : Auteur
  • PersonId : 1031548
Ines Ben-Jemaa
  • Fonction : Auteur
  • PersonId : 1057802
Arnaud Kaiser
  • Fonction : Auteur
Pascal Urien

Résumé

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.
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Dates et versions

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

Identifiants

  • HAL Id : hal-02353893 , version 1

Citer

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⟩
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