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Intrusion Detection System Classifier for VANET Based on Pre-processing Feature Extraction

  • Ayoob Ayoob
  • Ghaith KhalilEmail author
  • Morshed Chowdhury
  • Robin Doss
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 1113)

Abstract

Vehicular Ad-hoc Networks (VANETs) are gaining much interest and research efforts over recent years for it offers enhanced safety and improved travel comfort. However, security threats that are either seen in the ad-hoc networks or unique to VANET present considerable challenges. In this paper, we are presenting the intrusion detection classifier for VANET base on pre-processing feature extraction. This ID infrastructure novel is mainly introducing a new design feature for extraction mechanism a pre-processing feature-based classifier. In the beginning, we will extract the traffic stream structures and vehicle location features in the VANET model. Later an Algorithm Pre-processing feature-based classifier was designed for evaluating the IDS by using hierarchy learning process. Finally, an additional two-step validation mechanism was used to determine the abnormal vehicle messages accurately. The proposed method has better finding accuracy, stability, processing efficiency, and communication load.

Keywords

Vehicular Ad-hoc Network (VANET) Intrusion Detection System (IDS) IEEE 802.11p 
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References

  1. 1.
    Wu, J., Wang, Y.: Performance study of multi-path in VANETs and their impact on routing protocols. Wirel. Eng. Technol. 2011, 125–129 (2011)CrossRefGoogle Scholar
  2. 2.
    Pradweap, R.V., Hansdah, R.C.: A novel RSU-aided hybrid architecture for anonymous authentication (RAHAA) in VANET. In: Bagchi, A., Ray, I. (eds.) ICISS 2013. LNCS, vol. 8303, pp. 314–328. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-45204-8_24CrossRefGoogle Scholar
  3. 3.
    Al, G.: Technology Design Principle, Application and Controversies. Nov. Sci. Publ. (2018)Google Scholar
  4. 4.
    Rajarajan, C., Zaidi, K., Milojevic, M., Member, S., Rakocevic, V.: City research online city, University of London institutional repository host based intrusion detection for VANETs: a statistical approach to rogue node detection. IEEE Trans. Veh. Technol. 65(8), 6703–6714 (2016)CrossRefGoogle Scholar
  5. 5.
    LAN/MAN Standards Committee of the IEEE Computer Society Part 11 : Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications IEEE Computer Society, vol. 2012 (2012). ISBN 9780738172118Google Scholar
  6. 6.
    Hao, Z., Singh, G., Kamboj, E.S.: A review on multiple malicious and irrelevant packet detection in VANET. Int. J. Technol. Comput. 2 (2016)Google Scholar
  7. 7.
    Sedjelmaci, H., Senouci, S.M., Ansari, N.: Intrusion detection and ejection framework against lethal attacks in UAV-aided networks: a bayesian game-theoretic methodology. IEEE Trans. Syst. 18, 1143–1153 (2017)Google Scholar
  8. 8.
    Erritali, M., El, O.B.: A survey on VANET intrusion detection systems. Int. J. Eng. Technol. A 5, 1985–1989 (2013)Google Scholar
  9. 9.
    Zaidi, K., Milojevic, M.B., Member, S., Rakocevic, V.: Host-based intrusion detection for VANETs: a statistical approach to rogue node detection. IEEE Trans. Veh. Technol. 65, 6703–6714 (2016)CrossRefGoogle Scholar
  10. 10.
    Lu, R., Lin, X., Luan, T.H., Liang, X., Member, S., Shen, X.S.: Pseudonym changing at social spots: an effective strategy for location privacy in VANETs. IEEE Trans. Veh. Technol. 61, 86–96 (2012)CrossRefGoogle Scholar
  11. 11.
    Wex, P., Breuer, J., Held, A., Leinm, T.: Trust issues for vehicular ad hoc networks. Veh. Technol. Conf. 4, 2800–2804 (2008)Google Scholar
  12. 12.
    Bibhu Vimal, K.R.: Performance analysis of black hole attack in VANET. Comput. Netw. Inf. Secur. 5, 47–54 (2012)Google Scholar
  13. 13.
    Ayoob, A., Su, G., Al, G.: Hierarchical growing neural gas network (HGNG)-based semi cooperative feature classifier for IDS in vehicular ad hoc network (VANET). J. Sens. Actuator Netw. 7, 41 (2018)CrossRefGoogle Scholar
  14. 14.
    Minhas, U.F., Zhang, J.: Towards expanded trust management for agents in vehicular ad-hoc networks. Int. J. Comput. Intell. Theory Pract. 5 (2010)Google Scholar
  15. 15.
    Kargl, F., et al.: Secure vehicular communication systems: implementation, performance, and research challenges. IEEE Commun. Mag. 6, 177 (2008)Google Scholar
  16. 16.
    Al Gaith, K. (ed.): Analysis Radio Access Technology RFID/IEEE802.11p For VANET’s. RFID Technology: Design Principles, Applications and Controversies, Nova Publishing, pp. 51–70 (2018)Google Scholar
  17. 17.
    Hao, X., Hortelano, J., Sakiz, F., Sen, S.: Ad hoc networks survey paper a survey of attacks and detection mechanisms on intelligent transportation systems: VANETs and IoV. Ad Hoc Netw. 61, 33–50 (2017)CrossRefGoogle Scholar
  18. 18.
    Issariyakul, T., Hossain, E.: Introduction to Network Simulator NS2 (2012). ISBN 9781461414063CrossRefGoogle Scholar
  19. 19.
    Kolici, V., Oda, T., Sugihara, Y., Spaho, E., Ikeda, M., Barolli, L.: Performance evaluation of a VANET simulation system using NS-3 and SUMO considering number of vehicles and crossroad scenario. In: Proceedings of the - 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2015, pp. 22–27 (2015)Google Scholar
  20. 20.
    Behrisch, M., Bieker, L., Erdmann, J., Krajzewicz, D.: SUMO - simulation of urban mobility - an overview. In: Proceedings of the 3rd International Conference on Advances in System Simulation, pp. 63–68 (2011)Google Scholar
  21. 21.
    Sedjelmaci, H., Senouci, S.M., Ansari, N.: Intrusion detection and ejection framework against lethal attacks in UAV-aided networks: a Bayesian game-theoretic methodology. IEEE Trans. Intell. Transp. Syst. 18, 1143–1153 (2017)CrossRefGoogle Scholar
  22. 22.
    Mane, A.A.: Sybil attack in VANET. Int. J. Comput. Eng. Res. 06, 60–65 (2016)Google Scholar
  23. 23.
    Yan, S., Member, S., Malaney, R., Nevat, I., Peters, G.W.: Optimal information-theoretic wireless location verification. IEEE Transic. 63, 3410–3422 (2014)Google Scholar
  24. 24.
    Ayoob, A., et al.: Hierarchical Growing Neural Gas Network (HGNG)-Based Semi-Cooperative, 1st edn. Lambert Academic Publishing (2018). ISBN 978-613-9-89443-7Google Scholar
  25. 25.
    Ayoob, A., et al.: Performance Analysis of Routing Protocols for Mobile AD-HOC Network, 1st edn. Lambert Acadamic Publishing (2018). ISBN 978-613-9-93266-5Google Scholar
  26. 26.
    Al, G.: RFID Technology: Design Principles, Applications and Controversies. Nova Science Publishers, Inc. (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electronics and Information EngineeringHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.School of Information TechnologyDeakin UniversityGeelongAustralia

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