Skip to main content

Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks


An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road information conditions. However, VANETs share the same security concerns of legacy ad hoc networks. Unlike exiting works, we consider, in this paper, detection a common attack where nodes modify safety message or drop them. Unfortunately, detecting such a type of intrusion is a challenging problem since some packets may be lost or dropped in normal VANET due to congestion without malicious action. To mitigate these concerns, this paper presents a novel scheme for minimizing the invalidity ratio of VANET packets transmissions. In order to detect unusual traffic, the proposed scheme combines evidences from current as well as past behaviour to evaluate the trustworthiness of both data and nodes. A new intrusion detection scheme is accomplished through a four phases, namely, rule-based security filter, Dempster–Shafer adder, node’s history database, and Bayesian learner. The suspicion level of each incoming data is determined based on the extent of its deviation from data reported from trustworthy nodes. Dempster–Shafer’s theory is used to combine multiple evidences and Bayesian learner is adopted to classify each event in VANET into well-behaved or misbehaving event. The proposed solution is validated through extensive simulations. The results confirm that the fusion of different evidences has a significant positive impact on the performance of the security scheme compared to other counterparts.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    Alrawashdeh, K., & Purdy, C. (2016). Toward an online anomaly intrusion detection system based on deep learning. In 15th IEEE international conference on machine learning and applications (ICMLA) (pp. 195–200). IEEE.

  2. 2.

    Alsarhan, A., Al-Dubai, A. Y., Min, G., Zomaya, A. Y., & Bsoul, M. (2018). A new spectrum management scheme for road safety in smart cities. IEEE Transactions on Intelligent Transportation Systems, 19(11), 3496–3506.

    Article  Google Scholar 

  3. 3.

    Altwaijry, H. (2013). Bayesian based intrusion detection system. In IAENG transactions on engineering technologies (pp. 29–44). Springer.

  4. 4.

    Bahrololum, M., Salahi, E., & Khaleghi, M. (2009). Anomaly intrusion detection design using hybrid of unsupervised and supervised neural network. International Journal of Computer Networks & Communications (IJCNC), 1(2), 26–33.

    Google Scholar 

  5. 5.

    Bhoi, S. K., & Khilar, P. M. (2013). Vehicular communication: A survey. IET Networks, 3(3), 204–217.

    Article  Google Scholar 

  6. 6.

    Bitam, S., Mellouk, A., & Zeadally, S. (2015). Vanet-cloud: A generic cloud computing model for vehicular ad hoc networks. IEEE Wireless Communications, 22(1), 96–102.

    Article  Google Scholar 

  7. 7.

    Farid, D. M., & Rahman, M. Z. (2010). Attribute weighting with adaptive nbtree for reducing false positives in intrusion detection. arXiv preprint arXiv:1005.0919.

  8. 8.

    Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques (3rd ed.). Burlington: Morgan Kaufmann.

    MATH  Google Scholar 

  9. 9.

    Huang, Z., Ruj, S., Cavenaghi, M. A., Stojmenovic, M., & Nayak, A. (2014). A social network approach to trust management in vanets. Peer-to-Peer Networking and Applications, 7(3), 229–242.

    Article  Google Scholar 

  10. 10.

    Kang, M. J., & Kang, J. W. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PloS ONE, 11(6), e0155781.

    Article  Google Scholar 

  11. 11.

    Katar, C. (2006). Combining multiple techniques for intrusion detection. International Journal of Computer Science and Network Security, 6(2B), 208–218.

    Google Scholar 

  12. 12.

    Kumar, A., Singh, J. R., Singh, D., & Dewang, R. K. (2016). A historical feedback based misbehavior detection (hfmd) algorithm in vanet. In 2nd International conference on computational intelligence and networks (CINE) (pp. 15–22). IEEE.

  13. 13.

    Lo, N. W., & Tsai, H. C. (2007). Illusion attack on vanet applications-a message plausibility problem. In IEEE Globecom workshops (pp. 1–8). IEEE.

  14. 14.

    Ludwig, S. A. (2017). Intrusion detection of multiple attack classes using a deep neural net ensemble. In IEEE Symposium series on computational intelligence (SSCI) (pp. 1–7). IEEE.

  15. 15.

    Minhas, U. F., Zhang, J., Tran, T., & Cohen, R. (2010). A multifaceted approach to modeling agent trust for effective communication in the application of mobile ad hoc vehicular networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(3), 407–420.

    Article  Google Scholar 

  16. 16.

    Narla, S. R. (2013). The evolution of connected vehicle technology: From smart drivers to smart cars to self-driving cars. ITE Journal, 83(7), 22–26.

    Google Scholar 

  17. 17.

    Sedjelmaci, H., Senouci, S. M., & Abu-Rgheff, M. A. (2014). An efficient and lightweight intrusion detection mechanism for service-oriented vehicular networks. IEEE Internet of Things Journal, 1(6), 570–577.

    Article  Google Scholar 

  18. 18.

    Shafer, G. (1976). A mathematical theory of evidence (Vol. 42). Princeton: Princeton University Press.

    Book  Google Scholar 

  19. 19.

    Sharma, S., & Kaul, A. (2018). A survey on intrusion detection systems and honeypot based proactive security mechanisms in vanets and vanet cloud. Vehicular Communications, 12, 138–164.

    Article  Google Scholar 

  20. 20.

    Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50.

    Article  Google Scholar 

  21. 21.

    Soleymani, S. A., Abdullah, A. H., Zareei, M., Anisi, M. H., Vargas-Rosales, C., Khan, M. K., et al. (2017). A secure trust model based on fuzzy logic in vehicular ad hoc networks with fog computing. IEEE Access, 5, 15619–15629.

    Article  Google Scholar 

  22. 22.

    Sun, G., Sun, S., Sun, J., Yu, H., Du, X., & Guizani, M. (2019). Security and privacy preservation in fog-based crowd sensing on the internet of vehicles. Journal of Network and Computer Applications, 134, 89–99.

    Article  Google Scholar 

  23. 23.

    Vulimiri, A., Gupta, A., Roy, P., Muthaiah, S. N., & Kherani, A. A. (2010). Application of secondary information for misbehavior detection in vanets. In International conference on research in networking (pp. 385–396). Springer.

  24. 24.

    Wang, J., Zhang, Y., Wang, Y., & Gu, X. (2016). Rprep: A robust and privacy-preserving reputation management scheme for pseudonym-enabled vanets. International Journal of Distributed Sensor Networks, 12(3), 6138251.

    Article  Google Scholar 

  25. 25.

    Zaidi, K., Milojevic, M. B., Rakocevic, V., Nallanathan, A., & Rajarajan, M. (2015). Host-based intrusion detection for vanets: A statistical approach to rogue node detection. IEEE Transactions on Vehicular Technology, 65(8), 6703–6714.

    Article  Google Scholar 

  26. 26.

    Zhang, C., Chen, K., Zeng, X., & Xue, X. (2018). Misbehavior detection based on support vector machine and dempster-shafer theory of evidence in vanets. IEEE Access, 6, 59860–59870.

    Article  Google Scholar 

  27. 27.

    Zhang, J., Huang, L., Xu, H., Xiao, M., Guo, W. (2012). An incremental bp neural network based spurious message filter for vanet. In International conference on cyber-enabled distributed computing and knowledge discovery (pp. 360–367). IEEE.

Download references

Author information



Corresponding author

Correspondence to Ayoub Alsarhan.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alsarhan, A., Al-Ghuwairi, AR., Almalkawi, I.T. et al. Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks. Wireless Pers Commun 117, 3129–3152 (2021).

Download citation


  • Intrusion detection
  • Smart city
  • Malicious nodes
  • Security
  • Misbehavior detection