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

Issam Mahmoudi 1 Joseph Kamel 1, 2 Ines Ben-Jemaa 1 Arnaud Kaiser 1 Pascal Urien 3
1 IRT SystemX - IRT SystemX
2 Télécom ParisTech
3 INFRES - Département Informatique et Réseaux
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.
Keywords : Misbehavior Detection Machine Learning C-ITS
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Conference papers
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Submitted on : Thursday, November 7, 2019 - 2:56:19 PM
Last modification on : Wednesday, November 13, 2019 - 1:42:04 AM

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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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