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

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Vehicular Ad-hoc Networks for Smart Cities

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1144))

Abstract

Global misbehavior detection in Cooperative Intelligent Transport Systems (C–ITS) is carried out by a central entity named Misbehavior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle’s On–Board Units (OBUs) and by Road–Side 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.

This research work has been carried out in the framework of the Technological Research Institute SystemX, and therefore granted with public funds within the scope of the French Program Investissements d’avenir.

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Author information

Authors and Affiliations

  1. Institut de recherche technologique (IRT) SystemX, 91120, Palaiseau, France

    Issam Mahmoudi, Joseph Kamel, Ines Ben-Jemaa & Arnaud Kaiser

  2. Paris Descartes University, 75006, Paris, France

    Issam Mahmoudi

  3. Télécom ParisTech, 75013, Paris, France

    Joseph Kamel & Pascal Urien

Authors
  1. Issam Mahmoudi
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  2. Joseph Kamel
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  3. Ines Ben-Jemaa
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  4. Arnaud Kaiser
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  5. Pascal Urien
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Corresponding author

Correspondence to Joseph Kamel .

Editor information

Editors and Affiliations

  1. Telecom SudParis, Institut Polytechnique de Paris, Paris, France

    Anis Laouiti

  2. Capital University of Science & Technology, Islamabad, Pakistan

    Amir Qayyum

  3. Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia

    Mohamad Naufal Mohamad Saad

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Mahmoudi, I., Kamel, J., Ben-Jemaa, I., Kaiser, A., Urien, P. (2020). Towards a Reliable Machine Learning-Based Global Misbehavior Detection in C–ITS: Model Evaluation Approach. In: Laouiti, A., Qayyum, A., Mohamad Saad, M. (eds) Vehicular Ad-hoc Networks for Smart Cities. Advances in Intelligent Systems and Computing, vol 1144. Springer, Singapore. https://doi.org/10.1007/978-981-15-3750-9_6

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