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Detection of Threats in Cyberphysical Systems Based on Deep Learning Methods Using Multidimensional Time Series

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Abstract—

A method for detecting anomalies in the work of cyberphysical systems by analyzing a multidimensional time series is proposed. The method is based on the use of neural network technologies to predict the values ​​of the time series of the system data and to identify deviations between the predicted value and the current data obtained from the sensors and actuators. The results of experimental studies are presented, which testify to the effectiveness of the proposed solution.

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ACKNOWLEGMENTS

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

    M. O. Kalinin, D. S. Lavrova & A. V. Yarmak

Authors
  1. M. O. Kalinin
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  2. D. S. Lavrova
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  3. A. V. Yarmak
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Corresponding authors

Correspondence to M. O. Kalinin or D. S. Lavrova.

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Translated by S. Avodkova

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Kalinin, M.O., Lavrova, D.S. & Yarmak, A.V. Detection of Threats in Cyberphysical Systems Based on Deep Learning Methods Using Multidimensional Time Series. Aut. Control Comp. Sci. 52, 912–917 (2018). https://doi.org/10.3103/S0146411618080151

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