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Application of an Artificial Neural Network for Detection of Attacks in VANETs

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Abstract

This work contains results of developing an approach for detecting routing attacks in VANET networks using an artificial neural network. In the course of this work, such research methods as analysis and modeling were used to select the most promising approach for identifying routing attacks, as well as to develop a mock-up of a software system that detects Gray Hole attacks in VANETs. According to the results of experimental studies, the effectiveness of the developed software was evaluated.

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Funding

The reported study was funded by RFBR according to the research project no. 18-29-03102.

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Authors and Affiliations

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

    E. V. Malyshev, D. A. Moskvin & D. P. Zegzhda

Authors
  1. E. V. Malyshev
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  2. D. A. Moskvin
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  3. D. P. Zegzhda
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Corresponding authors

Correspondence to E. V. Malyshev, D. A. Moskvin or D. P. Zegzhda.

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The authors declare that they have no conflict of interest.

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Translated by K. Lazarev

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Malyshev, E.V., Moskvin, D.A. & Zegzhda, D.P. Application of an Artificial Neural Network for Detection of Attacks in VANETs. Aut. Control Comp. Sci. 53, 889–894 (2019). https://doi.org/10.3103/S0146411619080194

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  • DOI: https://doi.org/10.3103/S0146411619080194

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