Skip to main content
SpringerLink
Log in

Highly accurate sybil attack detection in vanet using extreme learning machine with preserved location

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Due to the development of transportation technology, the number of vehicles on the road has been exponential over the years. Road safety is one of the crucial tasks of the transportation department because of collations and accidents each year. Using a Vehicular ad hoc network (VANET) makes communication between vehicles possible and reduces the complexities in vehicle transportation. Privacy is one of the significant tasks in the VANET for a safe and uninterrupted transportation process. Sybil attack is one of the significant issues in the VANET in which attackers introduce dummy nodes to confuse or interrupt the other users in the network to reduce the performance to hack the data. This work proposes a new technique to detect and disconnect Sybil from the network to improve its performance. Historical and statistical data with Extreme Learning Machine is used to classify the Sybil attack in the VANET. This work improved the classification accuracy and network performance compared to the conventional Sybil node identification technique.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

There is no data associated with this study.

References

  1. Balaram, A., & Pushpa, S. (2016). Resilient privacy preservation scheme to detect sybil attacks in vehicular ad hoc networks. Indian Journal of Science and Technology, 9, 48.

    Article  Google Scholar 

  2. Paranjothi, A., & Mohammed, A., (2021). Enhancing security in vanets with efficient sybil attack detection using fog computing. arXiv preprint arXiv:2108.10319

  3. Mohamed, K., & Azer, M. A., (2012). Crypto-sap protocol for sybil attack prevention in vanets. In advances in computer communication and computational sciences: Proceedings of ic4s 2019, pp. 143–152. (Springer Singapore, 2021).

  4. Kadam, N., & Krovi, R. S., (2021). Machine learning approach of hybrid KSVN algorithm to detect DDoS attack in VANET. International Journal of Advanced Computer Science and Applications, 12(7).

  5. Min, M., Wang, W., Xiao, L., Xiao, Y., & Han, Z. (2021). Reinforcement learning-based sensitive semantic location privacy protection for VANETs. China Communication, 18(6), 244–260.

    Article  Google Scholar 

  6. Velayudhan, N. C., Anitha, A., Madhanan, M., (2021). Sybil attack detection and secure data transmission in VANET using CMEHA-DNN and MD5-ECC. Journal of Ambient Intelligence and Humanized Computing, 1–13.

  7. Chen, Ye., Lai, Y., Zhang, Z., Li, H., & Wang, Y. (2023). MDFD: A multi-source data fusion detection framework for Sybil attack detection in VANETs. Computer Networks, 224, 109608.

    Article  Google Scholar 

  8. Sefati, S. S, Tabrizi., S. G., (2022). Detecting sybil attack in vehicular ad-hoc networks (vanets) by using fitness function, signal strength index and throughput. Wireless Personal Communications, 1–21.

  9. Velayudhan, N. C., Anitha, A, & Madanan, M., (2022). Sybil attack with RSU detection and location privacy in urban VANETs: An efficient EPORP technique. Wireless Personal Communications, 1–29.

  10. Hamdan, S., Hudaib, A., & Awajan, A. (2021). Detecting Sybil attacks in vehicular ad hoc networks. Interantional Journal of Parallel, Emergent Distribution System, 36(2), 69–79.

    Article  Google Scholar 

  11. Azam, S., Maryum, B., Rabia, R., Rizvi, S. S., & kwon, S. J. (2022). Collaborative learning based Sybil attack detection in vehicular AD-HOC networks (VANETS). Sensors, 22(18), 6934.

    Article  Google Scholar 

  12. Kumar, S., Amol, V., & Sood, M. (2022). Sybil attack countermeasures in vehicular ad hoc networks. In 2022 international conference on communications, information, electronic and energy systems (CIEES), 1–6. IEEE.

  13. Shah, P., & Tanmay, K. (2021). Detecting sybil attack, black hole attack and dos attack in VANET using rsa algorithm. In 2021 emerging trends in industry 4.0 (ETI 4.0), 1–7. IEEE.

  14. Maleknasab Ardakani, M., Tabarzad, M. A., & Shayegan, M. A. (2022). Detecting sybil attacks in vehicular ad hoc networks using fuzzy logic and arithmetic optimization algorithm. The Journal of Supercomputing, 78(14), 16303–16335.

    Article  Google Scholar 

  15. Mohd, F. S., Gupta, B. K., & Zaidi, T., (2022). A hybrid framework to prevent VANET from Sybil attack. In 2022 5th international conference on multimedia, signal processing and communication technologies (IMPACT) 1–6. IEEE.

  16. Chaubey, N. K., & Yadav, D. (2022). Detection of Sybil attack in vehicular ad hoc networks by analyzing network performance. International Journal of Electrical and Computer Engineering, 12, 1703–1710.

    Google Scholar 

  17. Hussain, N., Maheshwary, P., Shukla, P. K., & Singh, A. (2022). Attack resilient and efficient protocol based on greedy perimeter coordinator routing—Mobility awareness for preventing the attack in the VANET. Wireless Personal Communication, 126(4), 2841–2868.

    Article  Google Scholar 

  18. Zulfahmi, H., Adriman, R., Arif, T. Y., Walidainy, H., & Fitria, M. (2022). sybil attack prediction on vehicle network using deep learning. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6(3), 499–504.

    Article  Google Scholar 

  19. Zhang, Z., Feng, T., Sikdar, B., & Wong, W. C., (2021). A flickering context-based mix strategy for privacy protection in vanets. In ICC 2021-IEEE international conference on communications, 1–6. IEEE.

  20. Wang, Y., Li, X., Zhang, X., Liu, X., & Weng, J. (2021). ARPLR: An all-round and highly privacy-preserving location-based routing scheme for VANETs. IEEE Transactions on Intelligent Transportation Systems, 23(9), 16558–16575.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, 500043, India

    Allam Balaram

  2. Department of Computer Science and Engineering, AVN Institute of Engineering and Technology, Hyderabad, Telangana, 501510, India

    Shaik Abdul Nabi

  3. Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, 500043, India

    Koppula Srinivas Rao

  4. Department of Computer Science and Engineering, Geetanjali College of Engineering and Technology, Hyderabad, Telangana, 501301, India

    Neeraja Koppula

Authors
  1. Allam Balaram
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaik Abdul Nabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Koppula Srinivas Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Neeraja Koppula
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Allam Balaram.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balaram, A., Nabi, S.A., Rao, K.S. et al. Highly accurate sybil attack detection in vanet using extreme learning machine with preserved location. Wireless Netw 29, 3435–3443 (2023). https://doi.org/10.1007/s11276-023-03399-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03399-1

Keywords

Search

Navigation