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Detection of Malicious Node in VANETs Using Digital Twin

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Big Data Intelligence and Computing (DataCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13864))

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Abstract

Vehicular ad-hoc network (VANET) plays an essential role in helping the development of smart cars and intelligent transportation systems. By easing congestion and speeding up data transfer, VANET makes it possible for connected cars to function more smoothly. However, VANETs are affected by different types of cyber attacks, such as distributed denial of services (DDoS) attacks. A digital twin (DT) is a replica of a physical system that operates in tandem with the actual thing, allowing for continuous monitoring and management. The DT prepares the way for the monitoring of a physical entity on a regular basis and for its automated management. The improved efficiency in keeping tabs on the physical world is largely attributable to DT. For this reason, academics are advocating for its use in a variety of settings. In this research, we use DT to solve the problem of identifying and stopping malignant nodes on a VANET infrastructure. In this paper, we proposed a framework that uses the concepts of DT for the identification of malignant nodes. Our suggested approach employs machine learning to distinguish between regular traffic and attack traffic.

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Author information

Authors and Affiliations

  1. Insights2Techinfo, Jaipur, India

    Varsha Arya

  2. Ronin Institute, Montclair, NJ, 07043, USA

    Akshat Gaurav

  3. International Center for AI and Cyber Security Research and Innovation and Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan

    Brij B. Gupta

  4. Lebanese American University, Beirut, 1102, Lebanon

    Brij B. Gupta

  5. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Ching-Hsien Hsu & Hojjat Baghban

  6. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan

    Ching-Hsien Hsu

  7. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

    Ching-Hsien Hsu

Authors
  1. Varsha Arya
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  2. Akshat Gaurav
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  3. Brij B. Gupta
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  4. Ching-Hsien Hsu
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  5. Hojjat Baghban
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Corresponding authors

Correspondence to Varsha Arya or Brij B. Gupta .

Editor information

Editors and Affiliations

  1. Asia University, Taichung, Taiwan

    Ching-Hsien Hsu

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Mengwei Xu

  3. University of New Brunswick, Fredericton, NB, Canada

    Hung Cao

  4. Asia University, Taichung, Taiwan

    Hojjat Baghban

  5. University of Fiji, Samabula, Fiji

    A. B. M. Shawkat Ali

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Arya, V., Gaurav, A., Gupta, B.B., Hsu, CH., Baghban, H. (2023). Detection of Malicious Node in VANETs Using Digital Twin. In: Hsu, CH., Xu, M., Cao, H., Baghban, H., Shawkat Ali, A.B.M. (eds) Big Data Intelligence and Computing. DataCom 2022. Lecture Notes in Computer Science, vol 13864. Springer, Singapore. https://doi.org/10.1007/978-981-99-2233-8_15

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  • DOI: https://doi.org/10.1007/978-981-99-2233-8_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2232-1

  • Online ISBN: 978-981-99-2233-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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