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A Machine Learning Based Approach to Detect Cyber-Attacks on Connected and Autonomous Vehicles (CAVs)

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Wireless Networks

Abstract

Connected and Autonomous Vehicles (CAVs) are gaining more interest and are growing steadily in recent years. They will surely become the backbone of next generation intelligent vehicles offering safe travels, comfort, reduced pollution, with many other beneficial features. However, with CAVs being equipped with high levels of automation and connectivity also opens several attack points or vulnerable points for adversaries to conduct attacks. Such security issues need to be addressed before commercialising CAVs. In this research paper, the focus is to develop a few machine learning models using different machine learning algorithms and evaluate them using defined evaluation criterions to identify and recommend the best suitable model for detecting attacks in CAVs. In addition, this paper also defines different terms related to CAVs such as CAV, CAV cyber security, CAV architecture and different vulnerabilities and risks present in the CAN bus. The paper then describes the different attacks possible on CAVs and the corresponding mitigation methods and detection techniques.

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Author information

Authors and Affiliations

  1. Northumbria University London, London, UK

    Safwan Abdul Nazaruddin

  2. Queen Mary University of London, London, UK

    Umair B. Chaudhry

Authors
  1. Safwan Abdul Nazaruddin
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  2. Umair B. Chaudhry
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Correspondence to Umair B. Chaudhry .

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Editors and Affiliations

  1. Department of Information Security and Cyber Criminology, Northumbria University London, London, UK

    Hamid Jahankhani

  2. Department of Computer Science and Engineering, University of Westminster, London, UK

    Ayman El Hajjar

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Nazaruddin, S.A., Chaudhry, U.B. (2023). A Machine Learning Based Approach to Detect Cyber-Attacks on Connected and Autonomous Vehicles (CAVs). In: Jahankhani, H., El Hajjar, A. (eds) Wireless Networks . Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-33631-7_6

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