Senior2Local: A Machine Learning Based Intrusion Detection Method for VANETs

  • Yi Zeng
  • Meikang QiuEmail author
  • Zhong Ming
  • Meiqin Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)


Vehicular Ad-hoc Network (VANET) is a heterogeneous network of resource-constrained nodes such as smart vehicles and Road Side Units (RSUs) communicating in a high mobility environment. Concerning the potentially malicious misbehaves in VANETs, real-time and robust intrusion detection methods are required. In this paper, we present a novel Machine Learning (ML) based intrusion detection methods to automatically detect intruders globally and locally in VANETs. Compared to previous Intrusion Detection methods, our method is more robust to the environmental changes that are typical in VANETs, especially when intruders overtake senior units like RSUs and Cluster Heads (CHs). The experimental results show that our approach can outperform previous work significantly when vulnerable RSUs exist.


ML Intrusion detection VANETs RSUs Game theory 



This work is supported by China NSFC 61836005 and 61672358; China NSFC 61728303 and the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (ICT1800417).


  1. 1.
    Alheeti, K.M.A., Gruebler, A., McDonald-Maier, K.D.: An intrusion detection system against black hole attacks on the communication network of self-driving cars. In: 2015 Sixth International Conference on Emerging Security Technologies (EST), pp. 86–91. IEEE (2015)Google Scholar
  2. 2.
    Chim, T.W., Yiu, S., Hui, L.C., Li, V.O.: Security and privacy issues for inter-vehicle communications in vanets. In: 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks Workshops, 2009, SECON Workshops 2009, pp. 1–3. IEEE (2009)Google Scholar
  3. 3.
    Gai, K., Qiu, M., Ming, Z., Zhao, H., Qiu, L.: Spoofing-jamming attack strategy using optimal power distributions in wireless smart grid networks. IEEE Trans. Smart Grid 8(5), 2431–2439 (2017)CrossRefGoogle Scholar
  4. 4.
    Kumar, N., Chilamkurti, N.: Collaborative trust aware intelligent intrusion detection in vanets. Comput. Electr. Eng. 40(6), 1981–1996 (2014)CrossRefGoogle Scholar
  5. 5.
    Li, W., Joshi, A., Finin, T.: SVM-case: an SVM-based context aware security framework for vehicular ad-hoc networks. In: 2015 IEEE 82nd Vehicular Technology Conference (VTC Fall), pp. 1–5. IEEE (2015)Google Scholar
  6. 6.
    Liu, J., Wan, J., Zeng, B., Wang, Q., Song, H., Qiu, M.: A scalable and quick-response software defined vehicular network assisted by mobile edge computing. IEEE Commun. Mag. 55(7), 94–100 (2017)CrossRefGoogle Scholar
  7. 7.
    Marti, S., Giuli, T.J., Lai, K., Baker, M.: Mitigating routing misbehavior in mobile adhoc networks. In: Proceedings of the 6th Annual International Conference on Mobilecomputing and Networking, pp. 255–265. ACM (2000)Google Scholar
  8. 8.
    Qian, Y., Moayeri, N.: Design of secure and application-oriented VANETs. In: 2008 IEEE Vehicular Technology Conference, VTC Spring 2008, pp. 2794–2799. IEEE (2008)Google Scholar
  9. 9.
    Qiu, M., Gai, K., Thuraisingham, B., Tao, L., Zhao, H.: Proactive user-centric secure data scheme using attribute-based semantic access controls for mobile clouds in financial industry. Futur. Gener. Comput. Syst. 80, 421–429 (2018)CrossRefGoogle Scholar
  10. 10.
    Sharma, S., Kaul, A.: A survey on intrusion detection systems and honeypot based proactive security mechanisms in VANETs and VANET cloud. Vehic. Commun. (2018)Google Scholar
  11. 11.
    Wahab, O.A., Bentahar, J., Otrok, H., Mourad, A.: Towards trustworthy multi-cloud services communities: a trust-based hedonic coalitional game. IEEE Trans. Serv. Comput. 11(1), 184–201 (2018)CrossRefGoogle Scholar
  12. 12.
    Wahab, O.A., Mourad, A., Otrok, H., Bentahar, J.: CEAP: SVM-based intelligent detection model for clustered vehicular ad hoc networks. Expert. Syst. Appl. 50, 40–54 (2016)CrossRefGoogle Scholar
  13. 13.
    Wahab, O.A., Otrok, H., Mourad, A.: Vanet QoS-OLSR: QoS-based clustering protocol for vehicular ad hoc networks. Comput. Commun. 36(13), 1422–1435 (2013)CrossRefGoogle Scholar
  14. 14.
    Zeng, X., Bagrodia, R., Gerla, M.: GloMoSim: a library for parallel simulation of large-scale wireless networks. In: ACM SIGSIM Simulation Digest, vol. 28, pp. 154–161. IEEE Computer Society (1998)Google Scholar
  15. 15.
    Zhu, M., et al.: Public vehicles for future urban transportation. IEEE Trans. Intell. Transp. Syst. 17(12), 3344–3353 (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Electronic and Information EngineeringXidian UniversityXi’anChina
  2. 2.College of Computer ScienceShenzhen UniversityShenzhenChina
  3. 3.College of Electrical EngineeringZhejiang UniversityHangzhouChina

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