Skip to main content

Paving the Way: Advancing V2X Safety Through Innovative Attack Generation and Analysis Framework (V2X-SAF)

  • Conference paper
  • First Online:
Information Systems Security (ICISS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15416))

Included in the following conference series:

  • 313 Accesses

Abstract

Vehicle-to-Everything (V2X) communication is crucial for the advancement of modern transportation systems, enabling real-time, dependable, and actionable data exchange. This technology facilitates the dissemination of Basic Safety Messages (BSMs) between vehicles and infrastructure, thereby enhancing safety, mobility, and environmental applications. Ensuring the integrity and accuracy of V2X data is vital for effective decision-making. This paper leverages the VEINS simulation framework to introduce 25 new sophisticated attacks aimed at four newly developed safety applications. These applications have been meticulously developed from scratch. Moreover, we introduce a multi-label attack generation technique, enabling multiple simultaneous attacks within a single data packet. For instance, coordinated attacks where speed adjustments are synchronized with changes in acceleration, increasing their complexity and detection difficulty. Central to our work are the advanced detection mechanisms designed to operate on Roadside Units (RSUs). These mechanisms employ trained algorithms to identify and neutralize malicious packets in real-time simulations, significantly bolstering the security of V2X systems. This comprehensive framework not only aims to reinforce the security infrastructure of V2X networks but also to guide standardization efforts and inform deployment strategies. Additionally, the implementation of digital certificates for digital signatures serves as a primary defense against malicious entities, ensuring the authenticity and integrity of V2X communications. Our objective is to provide the security community with an effective tool for developing a resilient and secure V2X ecosystem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Valentini, E.P., Filho, G.P.R., De Grande, R.E., Ranieri, C.M., Júnior, L.A.P., Meneguette, R.I.: A novel mechanism for misbehavior detection in vehicular networks. IEEE Access 11, 68113–68126 (2023). https://doi.org/10.1109/ACCESS.2023.3292055

    Article  Google Scholar 

  2. Ansari, M.R., Petit, J., Monteuuis, J.P., Chen, C.: VASP: V2X application spoofing platform. In: Proceedings Inaugural International Symposium on Vehicle Security & Privacy, NDSS-symposium (2023)

    Google Scholar 

  3. Shanmuganathan, V., Suresh, A.: LSTM-Markov based efficient anomaly detection algorithm for IoT environment. Appl. Soft Comput. 136, 110054 (2023). https://doi.org/10.1016/j.asoc.2023.110054. ISSN 1568-4946

    Article  Google Scholar 

  4. Sun, F., Brooks, R., Comert, G., Tusing, N.: Side-channel security analysis of connected vehicle communications using hidden Markov models. IEEE Trans. Intell. Transp. Syst. 23, 1–13 (2022). https://doi.org/10.1109/TITS.2022.3164779

    Article  Google Scholar 

  5. Sultan, D., Javaid, Q., Malik, A., Al-Turjman, F., Khan, M.: Collaborative-trust approach towards malicious node detection in vehicular ad-hoc networks. Environ. Dev. Sustain. 24, 1–19 (2022). https://doi.org/10.1007/s10668-021-01632-5

    Article  Google Scholar 

  6. Gonçalves, F., Macedo, J., Santos, A.: Evaluation of VANET datasets in context of an intrusion detection system. In: 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Hvar, Croatia, pp. 1–6 (2021). https://doi.org/10.23919/SoftCOM52868.2021.9559058

  7. Alladi, T., Gera, B., Agrawal, A., Chamola, V., Yu, F.R.: DeepADV: a deep neural network framework for anomaly detection in VANETs. IEEE Trans. Veh. Technol. 70(11), 12013–12023 (2021). https://doi.org/10.1109/TVT.2021.3113807

    Article  Google Scholar 

  8. Gonçalves, F., et al.: Synthesizing datasets with security threats for vehicular ad-hoc networks. In: GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, pp. 1–6 (2020). https://doi.org/10.1109/GLOBECOM42002.2020.9348149

  9. Kamel, J., Ansari, M.R., Petit, J., Kaiser, A., Jemaa, I.B., Urien, P.: Simulation framework for misbehavior detection in vehicular networks. IEEE Trans. Veh. Technol. 69(6), 6631–6643 (2020). https://doi.org/10.1109/TVT.2020.2984878

    Article  Google Scholar 

  10. Alladi, T., Chamola, V., Sikdar, B., Choo, K.-K.R.: Consumer IoT: security vulnerability case studies and solutions. IEEE Cons. Electron. Maga. 9(2), 17–25 (2020). https://doi.org/10.1109/MCE.2019.2953740

    Article  Google Scholar 

  11. Li, Y., Luo, Q., Liu, J., Guo, H., Kato, N.: TSP security in intelligent and connected vehicles: challenges and solutions. IEEE Wirel. Commun. 26(3), 125–131 (2019). https://doi.org/10.1109/MWC.2019.1800289

    Article  Google Scholar 

  12. Lu, Z., Qu, G., Liu, Z.: A survey on recent advances in vehicular network security, trust, and privacy. IEEE Trans. Intell. Transp. Syst. 20(2), 760–776 (2019). https://doi.org/10.1109/TITS.2018.2818888

    Article  Google Scholar 

  13. Lu, R., Zhang, L., Ni, J., Fang, Y.: 5G vehicle-to-everything services: gearing up for security and privacy. Proc. IEEE 108(2), 373–389 (2020). https://doi.org/10.1109/JPROC.2019.2948302

    Article  Google Scholar 

  14. Bansal, G., Naren, N., Chamola, V., Sikdar, B., Kumar, N., Guizani, M.: Lightweight mutual authentication protocol for V2G using physical unclonable function. IEEE Trans. Veh. Technol. 69(7), 7234–7246 (2020). https://doi.org/10.1109/TVT.2020.2976960

    Article  Google Scholar 

  15. Alladi, T., Chakravarty, S., Chamola, V., Guizani, M.: A lightweight authentication and attestation scheme for in-transit vehicles in IoV scenario. IEEE Trans. Veh. Technol. 69(12), 14188–14197 (2020). https://doi.org/10.1109/TVT.2020.3038834

    Article  Google Scholar 

  16. Monteuuis, J.-P., Zhang, J., Mafrica, S., Servel, A., Petit, J.: Attacker model for connected and automated vehicles (2018). https://doi.org/10.1145/3273946.3273951

  17. https://veins.car2x.org/

  18. Kamel, J., Wolf, M., van der Hei, R.W., Kaiser, A., Urien, P., Kargl, F.: VeReMi extension: a dataset for comparable evaluation of misbehavior detection in VANETs. In: ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, pp. 1–6 (2020). https://doi.org/10.1109/ICC40277.2020.9149132

  19. https://www.sae.org/standards/content/j2945/1_201603

  20. Lopez, P.A., et al.: Microscopic traffic simulation using SUMO. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, pp. 2575–2582 (2018). https://doi.org/10.1109/ITSC.2018.8569938

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shubham Tomar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tomar, S., Tripathi, M. (2025). Paving the Way: Advancing V2X Safety Through Innovative Attack Generation and Analysis Framework (V2X-SAF). In: Patil, V.T., Krishnan, R., Shyamasundar, R.K. (eds) Information Systems Security. ICISS 2024. Lecture Notes in Computer Science, vol 15416. Springer, Cham. https://doi.org/10.1007/978-3-031-80020-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-80020-7_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-80019-1

  • Online ISBN: 978-3-031-80020-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics