Detection of Malicious Node in VANETs Using Digital Twin

Varsha Arya; Akshat Gaurav; Brij B. Gupta; Ching Hsien Hsu; Hojjat Baghban

doi:10.1007/978-981-99-2233-8_15

Detection of Malicious Node in VANETs Using Digital Twin

Varsha Arya^*
, Akshat Gaurav
, Brij B. Gupta
, Ching Hsien Hsu
, Hojjat Baghban

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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.

Original language	English
Title of host publication	Big Data Intelligence and Computing - International Conference, DataCom 2022, Proceedings
Editors	Ching-Hsien Hsu, Mengwei Xu, Hung Cao, Hojjat Baghban, A. B. Shawkat Ali
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	204-212
Number of pages	9
ISBN (Print)	9789819922321
DOIs	https://doi.org/10.1007/978-981-99-2233-8_15
State	Published - 2023
Externally published	Yes
Event	International Conference on Big Data Intelligence and Computing, DataCom 2022 - Denarau, Fiji Duration: 08 12 2022 → 10 12 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13864 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Big Data Intelligence and Computing, DataCom 2022
Country/Territory	Fiji
City	Denarau
Period	08/12/22 → 10/12/22

Bibliographical note

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

Keywords

DDoS
Digital Twin
VANET

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-99-2233-8_15

Cite this

Arya, V., Gaurav, A., Gupta, B. B., Hsu, C. H., & Baghban, H. (2023). Detection of Malicious Node in VANETs Using Digital Twin. In C.-H. Hsu, M. Xu, H. Cao, H. Baghban, & A. B. Shawkat Ali (Eds.), Big Data Intelligence and Computing - International Conference, DataCom 2022, Proceedings (pp. 204-212). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13864 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-2233-8_15

Arya, Varsha ; Gaurav, Akshat ; Gupta, Brij B. et al. / Detection of Malicious Node in VANETs Using Digital Twin. Big Data Intelligence and Computing - International Conference, DataCom 2022, Proceedings. editor / Ching-Hsien Hsu ; Mengwei Xu ; Hung Cao ; Hojjat Baghban ; A. B. Shawkat Ali. Springer Science and Business Media Deutschland GmbH, 2023. pp. 204-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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Arya, V, Gaurav, A, Gupta, BB, Hsu, CH & Baghban, H 2023, Detection of Malicious Node in VANETs Using Digital Twin. in C-H Hsu, M Xu, H Cao, H Baghban & AB Shawkat Ali (eds), Big Data Intelligence and Computing - International Conference, DataCom 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13864 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 204-212, International Conference on Big Data Intelligence and Computing, DataCom 2022, Denarau, Fiji, 08/12/22. https://doi.org/10.1007/978-981-99-2233-8_15

Detection of Malicious Node in VANETs Using Digital Twin. / Arya, Varsha; Gaurav, Akshat; Gupta, Brij B. et al.
Big Data Intelligence and Computing - International Conference, DataCom 2022, Proceedings. ed. / Ching-Hsien Hsu; Mengwei Xu; Hung Cao; Hojjat Baghban; A. B. Shawkat Ali. Springer Science and Business Media Deutschland GmbH, 2023. p. 204-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13864 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hsu, Ching Hsien

AU - Baghban, Hojjat

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Arya V, Gaurav A, Gupta BB, Hsu CH, Baghban H. Detection of Malicious Node in VANETs Using Digital Twin. In Hsu CH, Xu M, Cao H, Baghban H, Shawkat Ali AB, editors, Big Data Intelligence and Computing - International Conference, DataCom 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 204-212. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-2233-8_15