Skip to main content

An authentication and plausibility model for big data analytic under LOS and NLOS conditions in 5G-VANET


The exchange of correct and reliable data among legitimate nodes is one of the most important challenges in vehicular ad hoc networks (VANETs). Malicious nodes and obstacles, by generating inaccurate information, have a negative impact on the security of 5G-VANET. The big data generated in the vehicular network is also an issue in the security of VANET. To this end, a security model based on authentication and plausibility is proposed to improve the safety of network named ‘AFPM’. In the first layer, an authentication mechanism using edge nodes along with 5G is proposed to deal with the illegitimate nodes who enter the network and broadcast wrong information. In the authentication mechanism, because of the growth of the connected vehicles to the edge nodes that lead to generating big data and hence the inappropriateness of the traditional data structures, cuckoo filter, as a space-efficient probabilistic data structure, is used. In the second layer, a plausibility model by performing fuzzy logic is presented to cope with inaccurate information. The plausibility model is based on detection of inconsistent data involved in the event message. The plausibility model not only tackles with inaccurate, incomplete, and inaccuracy data but also deals with misbehaviour nodes under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. All obtained results are validated through well-known evaluation measures such as F-measure and communication overhead. The results presented in this paper demonstrate that the proposed security model possesses a better performance in comparison with the existing studies.

This is a preview of subscription content, access via your institution.


  1. Anjum S S, Noor R M, Anisi M H., Review on MANET based communication for search and rescue operations. Wirel Pers Commun, 2017, 94: 31–52

    Article  Google Scholar 

  2. Soleymani S A, Abdullah A H, Hassan W H, et al., Trust management in vehicular ad hoc network: a systematic review. EURASIP J Wirel Commun Netw, 2015, 2015: 146

    Article  Google Scholar 

  3. Al-Sultan S, Al-Doori M M, Al-Bayatti A H, et al., A comprehensive survey on vehicular ad hoc network. J Netw Comput Appl, 2014, 37: 380–392

    Article  Google Scholar 

  4. Hua L C, Anisi M H, Yee P L, et al., Social networking-based cooperation mechanisms in vehicular ad-hoc network-a survey. Vehicular Commun, 2017, 10: 57–73

    Article  Google Scholar 

  5. Sedjelmaci H, Senouci S M, Abu-Rgheff M A., An efficient and lightweight intrusion detection mechanism for service-oriented vehicular networks. IEEE Int Things J, 2014, 6: 570–577

    Article  Google Scholar 

  6. Bismeyer N, Mauthofer S, Bayarou K M, et al. Assessment of node trustworthiness in vanets using data plausibility checks with particle filters. In: Proceedings of 2012 IEEE Vehicular Networking Conference (VNC), 2012. 78–85

  7. Manvi S S, Tangade S., A survey on authentication schemes in VANETs for secured communication. Vehicular Commun, 2017, 9: 19–30

    Article  Google Scholar 

  8. Garg S, Singh A, Kaur K, et al., Edge computing-based security framework for big data analytics in VANETs. IEEE Netw, 2019, 33: 72–81

    Article  Google Scholar 

  9. Engoulou R G, Bellache M, Pierre S, et al., VANET security surveys. Comput Commun, 2014, 44: 1–13

    Article  Google Scholar 

  10. Soleymani S A, Abdullah A H, Zareei M, et al., A secure trust model based on fuzzy logic in vehicular ad hoc networks with fog computing. IEEE Access, 2017, 5: 15619–15629

    Article  Google Scholar 

  11. Pournaghi S M, Zahednejad B, Bayat M, et al., NECPPA: a novel and efficient conditional privacy-preserving authentication scheme for VANET. Comput Netw, 2018, 134: 78–92

    Article  Google Scholar 

  12. Lu R X, Lin X D. ECPP: efficient conditional privacy preservation protocol. In: Proceedings of the 27th Conference on Computer Communications, 2015. 51–70

  13. Huang D, Misra S, Verma M, et al., PACP: an efficient pseudonymous authentication-based conditional privacy protocol for VANETs. IEEE Trans Intell Transp Syst, 2011, 12: 736–746

    Article  Google Scholar 

  14. Tangade S, Manvi S S, Lorenz P., Decentralized and scalable privacy-preserving authentication scheme in VANETs. IEEE Trans Vehicular Tech, 2018, 67: 8647–8655

    Article  Google Scholar 

  15. Chen Y M, Wei Y C., A beacon-based trust management system for enhancing user centric location privacy in VANETs. J Commun Netw, 2013, 15: 153–163

    Article  Google Scholar 

  16. Lo N-W, Tsai H-C. Illusion attack on vanet applications-a message plausibility problem. In: Proceedings of 2007 IEEE Globecom Workshops, 2007. 1–8

  17. Boeira F, Asplund M, Barcellos M P. Vouch: a secure proof-of-location scheme for vanets. In: Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, 2018. 241–248

  18. Goudarzi S, Abdullah A H, Mandala S, et al. A systematic review of security in vehicular ad hoc network. In: Proceedings of the 2nd Symposium on Work Sheet Control Number, 2013. 1–10

  19. Singh A, Garg S, Kaur R, et al., Probabilistic data structures for big data analytics: a comprehensive review. Knowledge-Based Syst, 2019, 188: 104987

    Article  Google Scholar 

  20. Bender M A, Farach-Colton M, Johnson R, et al., Don’t thrash: how to cache your hash on ash. Proc VLDB Endow, 2012, 5: 1627–1637

    Article  Google Scholar 

  21. Fan B, Andersen D G, Kaminsky M, et al. Cuckoo filter: practically better than bloom. In: Proceedings of the 10th ACM International on Conference on Emerging Networking Experiments and Technologies, 2014. 75–88

  22. Pagh R, Rodler F F., Cuckoo hashing. J Algorithms, 2004, 51: 122–144

    MathSciNet  Article  Google Scholar 

  23. Soleymani S A, Abdullah A H, Anisi M H, et al., BRAIN-F: beacon rate adaption based on fuzzy logic in vehicular ad hoc network. Int J Fuzzy Syst, 2017, 19: 301–315

    Article  Google Scholar 

  24. Limouchi E, Mahgoub I. BEFLAB: bandwidth efficient fuzzy logic-assisted broadcast for VANET. In: Proceedings of IEEE Symposium on Computational Intelligence, 2016. 1–8

  25. Khan S, Mauri J L. Security for Multihop Wireless Networks. Boca Raton: CRC Press, 2014

    Book  Google Scholar 

  26. Shaikh R A, Alzahrani A S., Intrusion-aware trust model for vehicular ad hoc networks. Secur Commun Netw, 2014, 7: 1652–1669

    Article  Google Scholar 

  27. Huang Z. On reputation and data-centric misbehavior detection mechanisms for VANET. Dissertation for Ph.D. Degree. Ottawa: University of Ottawa, 2011

    Google Scholar 

  28. Abumansoor O, Boukerche A., A secure cooperative approach for nonline-of-sight location verification in VANET. IEEE Trans Vehicular Tech, 2011, 61: 275–285

    Article  Google Scholar 

  29. Shah S, Shah B, Amin A, et al., Compromised user credentials detection in a digital enterprise using behavioral analytics. Future Gener Comput Syst, 2019, 93: 407–417

    Article  Google Scholar 

  30. Davis J, Goadrich M. The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, 2006. 233–240

  31. Villalba L J G, Orozco A L S, Cabrera A T, et al., Routing protocols in wireless sensor networks. Sensors, 2009, 9: 8399–8421

    Article  Google Scholar 

  32. Kumar N, Singh Y. Routing protocols in wireless sensor networks. In: Handbook of Research on Advanced Wireless Sensor Network Applications, Protocols, and Architectures. Hershey: IGI Global, 2017. 86–128

    Chapter  Google Scholar 

Download references


This work was supported by Ministry of Education, Malaysia, in collaboration with the Research Management Center, Universiti Teknologi Malaysia (Grant No. Q.J130000.2451.04G80), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (Grant No. GGPM-2020-029), and partially supported by King Saud University (Grant No. RSP-2019/12), Riyadh, Saudi Arabia.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to M. H. Anisi or Sh. Goudarzi.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Soleymani, S.A., Anisi, M.H., Abdullah, A.H. et al. An authentication and plausibility model for big data analytic under LOS and NLOS conditions in 5G-VANET. Sci. China Inf. Sci. 63, 220305 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • authentication
  • plausibility
  • fuzzy logic
  • cuckoo filter
  • 5G-VANET
  • big data