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Using Neural Networks to Detect Internal Intruders in VANETs

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Abstract—

This article considers ensuring protection of Vehicular Ad-Hoc Networks (VANET) against malicious nodes. Characteristic performance features of VANETs and threats are analyzed, and current attacks identified. The proposed approach to security provision relies on radial basis neural networks and makes it possible to identify malicious nodes by indicators of behavior.

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REFERENCES

  1. Sabahi, F., Impact of threats on vehicular adhoc network security, Int. J. Comput. Theory Eng., 2012, vol. 4, no. 5, p. 840.

    Article  Google Scholar 

  2. Zahariadis, T., et al., Energy-aware secure routing for large wireless sensor networks, WSEAS Trans. Commun., 2009, vol. 8, no. 9, pp. 981–991.

    Google Scholar 

  3. Awad, M., et al., Prediction of time series using RBF neural networks: A new approach of clustering, Int. Arab J. Inf. Technol., 2009, vol. 6, no. 2, pp. 138–143.

    Google Scholar 

  4. Haykin, S., et al., Neural Networks and Learning Machines, Upper Saddle River, NJ: Pearson, 2009, vol. 3.

    Google Scholar 

  5. Jirina, M., Radial Basis Function Neural Network with Example Weights and LMS Linear Regression Weight Setting, 2001.

  6. Kalinin, M., Krundyshev, V., Zegzhda, P., and Belenko, V., Network security architectures for VANET, Proceedings of the 10th International Conference on Security of Information and Networks, ACM, 2017, pp. 73–79.

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ACKNOWLEDGMENTS

The project results are achieved using the resources of supercomputer center of Peter the Great St.Petersburg Polytechnic University – SCC “Polytechnichesky” (www.spbstu.ru).

The project is financially supported by Ministry of Science and Higher Education of the Russian Federation, Federal Program “Researching and Development in Priority Directions of Scientific and Technological Sphere in Russia within 2014–2020” (Contract no. 14.575.21.0131, September 26, 2017, unique identifier RFMEFI57517X0131).

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Authors and Affiliations

  1. Peter the Great St.Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    T. D. Ovasapyan, D. A. Moskvin & M. O. Kalinin

Authors
  1. T. D. Ovasapyan
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  2. D. A. Moskvin
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  3. M. O. Kalinin
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Corresponding authors

Correspondence to T. D. Ovasapyan or M. O. Kalinin.

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Translated by S. Kuznetsov

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Ovasapyan, T.D., Moskvin, D.A. & Kalinin, M.O. Using Neural Networks to Detect Internal Intruders in VANETs. Aut. Control Comp. Sci. 52, 954–958 (2018). https://doi.org/10.3103/S0146411618080199

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  • DOI: https://doi.org/10.3103/S0146411618080199

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