Misbehavior Detection Based on Ensemble Learning in VANET

  • Jyoti Grover
  • Vijay Laxmi
  • Manoj Singh Gaur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7135)

Abstract

Detection of misbehaviors in Vehicular Ad Hoc Networks (VANETs) using machine learning methods has not been investigated extensively. In VANET, an illegitimate vehicle may transmit inaccurate messages to trigger an un- avoidable situation. In this paper, we present an ensemble based machine learning approach to classify misbehaviors in VANET. The performance of classifiers used for classification depends on the induction algorithms. We exploit the strengths of different classifiers using an ensemble method that combines the results of individual classifiers into one final result in order to achieve higher detection accuracy. Proposed security framework to classify different types of misbehaviors is implemented using WEKA. Features of nodes participating in VANET are extracted by performing experiments in NCTUns-5.0 simulator with different simulation scenarios (varying the number of legitimate and misbehaving nodes). We evaluate ensemble method using five different base inducers (Naive Bayes, IBK, RF, J48, Adaboost(J48)). We also show that ensemble based approach is more efficient in classifying multiple misbehaviors present in VANET as compared to base classifiers used for classification.

Keywords

Random Forest Packet Delivery Ratio Malicious Node Ensemble Learn True Negative Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Aijaz, A., Bochow, B., Dtzer, F., Festag, A., Gerlach, M., Kroh, R., Leinmller, T.: Attacks on Inter Vehicle Communication Systems - an Analysis. In: Proc. WIT, pp. 189–194 (2006)Google Scholar
  2. 2.
    Grover, J., Prajapati, N.K., Laxmi, V., Gaur, M.S.: Machine Learning Approach for Multiple Misbehavior Detection in VANET. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds.) ACC 2011. CCIS, vol. 192, pp. 644–653. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    University of Waikato: Open Source Machine Learning Software Weka, http://www.cs.waikato.ac.nz/ml/weka
  4. 4.
    Raya, M., Hubaux, J.P.: Securing Vehicular Ad Hoc Networks. Journal of Computer Security 15(1), 39–68 (2007)Google Scholar
  5. 5.
    Ghosh, M., Varghese, A., Kherani, A.A., Gupta, A.: Distributed Misbehavior Detection in VANETs. In: Proceedings of the 2009 IEEE Conference on Wireless Communications and Networking Conference, pp. 2909–2914. IEEE (2009)Google Scholar
  6. 6.
    Ghosh, M., Varghese, A., Kherani, A.A., Gupta, A., Muthaiah, S.N.: Detecting Misbehaviors in VANET with Integrated Root-cause Analysis. Ad Hoc Netw. 8, 778–790 (2010)CrossRefGoogle Scholar
  7. 7.
    Raya, M., Papadimitratos, P., Gligor, V.D., Hubaux, J.P.: On data centric trust establishment in ephemeral ad hoc networks. In: IEEE INFOCOM (2008)Google Scholar
  8. 8.
    Raya, M., Papadimitratos, P., Aad, I., Jungels, D., Hubaux, J.P.: Eviction of Misbehaving and Faulty nodes in Vehicular Networks. IEEE Journal on Selected Areas in Communications, Special Issue on Vehicular Networks 25(8), 1557–1568 (2007)CrossRefGoogle Scholar
  9. 9.
    Grover, J., Gaur, M.S., Laxmi, V.: Position Forging Attacks in Vehicular Ad Hoc Networks: Implementation, Impact and Detection. In: Proceedings of the 7th International Wireless Communications and Mobile Computing Conference (IWCMC 2011), pp. 701–706. IEEE (2011)Google Scholar
  10. 10.
    Grover, J., Kumar, D., Sargurunathan, M., Gaur, M.S., Laxmi, V.: Performance Evaluation and Detection of Sybil Attacks in Vehicular Ad-Hoc Networks. In: Meghanathan, N., Boumerdassi, S., Chaki, N., Nagamalai, D. (eds.) CNSA 2010. CCIS, vol. 89, pp. 473–482. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Grover, J., Gaur, M.S., Laxmi, V.: A Novel Defense Mechanism against Sybil Attacks in VANET. In: Proceedings of the 3rd International Conference on Security of Information and Networks, pp. 249–255. ACM (2010)Google Scholar
  12. 12.
    Schmidt, R.K., Leinmuller, T., Schoch, E., Held, A., Schafer, G.: Vehicle Behavior Analysis to Enhance Security in VANETs. In: Vehicle to Vehicle Communication, V2VCOM (2008)Google Scholar
  13. 13.
    Kim, T.H., Studer, H., Dubey, R., Zhang, X., Perrig, A., Bai, F., Bellur, B., Iyer, A.: VANET Alert Endorsement Using Multi-Source Filters. In: Proceedings of the Seventh ACM International Workshop on Vehicular Internetworking, pp. 51–60. ACM (2010)Google Scholar
  14. 14.
    NCTUns 5.0, Network Simulator and Emulator, http://NSL.csie.nctu.edu.tw/nctuns.html
  15. 15.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann (1999)Google Scholar
  16. 16.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letter 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jyoti Grover
    • 1
  • Vijay Laxmi
    • 1
  • Manoj Singh Gaur
    • 1
  1. 1.Department of Computer EngineeringMalaviya National Institute of TechnologyJaipurIndia

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