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A Machine Learning Framework for Intrusion Detection in VANET Communications

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Emerging Trends in Cybersecurity Applications

Abstract

Intelligent transportation system (ITS) is a promising technology to enhance driving safety and efficiency within smart cities. It involves public transportation management, infrastructure control, and road safety. Its main purpose is to avoid risks and accidents, reduce traffic congestion, and ensure safety for road users.

Vehicular ad hoc networks (VANETs) are core components of ITS where wireless communications between vehicles, as well as between vehicles and infrastructure, are possible to allow exchanging road, traffic, or infotainment information. VANETs are vulnerable to several security attacks that may compromise the driver’s safety. Using misbehavior detection approaches and information analysis demonstrated promising results in securing VANETs. In this context, machine learning (ML) techniques proved their efficiency in detecting attacks and misbehavior, especially zero-day attacks.

The goal of this chapter is two-fold. First, we intend to analyze the security issue in VANET by reviewing the most important vulnerabilities and proposed countermeasures. In a second part, we define a novel framework for designing an intrusion detection system (IDS) for vehicle-to-everything (V2X) communications. Furthermore, we use our framework for analyzing the efficiency of both standalone and ensemble ML approaches in detecting DOS and DDOS VANET attacks by means of extensive simulations conducted using the VDOS-LRS dataset.

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Acknowledgment

We would like to thank the research team of the Networks and Systems Laboratory-LRS, Department of Computer Science, Badji Mokhtar University, Annaba, Algeria, for sharing with us their work on the VDOS-LR security dataset.

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Authors and Affiliations

  1. Centre de Recherche en Informatique, Université Paris 1 Panthéon Sorbonne, Paris, France

    Nourhene Ben Rabah

  2. ESIEE-IT, Pontoise, France

    Nourhene Ben Rabah

  3. National School of Computer Science, University of Manouba, Manouba, Tunisia

    Hanen Idoudi

Authors
  1. Nourhene Ben Rabah
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  2. Hanen Idoudi
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Corresponding author

Correspondence to Hanen Idoudi .

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Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Kent Institute Australia, Sydney, NSW, Australia

    Abeer Alsadoon

  3. Ulster University, Ulster, UK

    Cathryn Peoples

  4. EPITA Engineering School, Paris, France

    Nour El Madhoun

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Ben Rabah, N., Idoudi, H. (2023). A Machine Learning Framework for Intrusion Detection in VANET Communications. In: Daimi, K., Alsadoon, A., Peoples, C., El Madhoun, N. (eds) Emerging Trends in Cybersecurity Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-09640-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-09640-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09639-6

  • Online ISBN: 978-3-031-09640-2

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