Detection of Threats in Cyberphysical Systems Based on Deep Learning Methods Using Multidimensional Time Series (original) (raw)

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.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

REFERENCES

  1. Vasil’ev, Y.S., Zegzhda, P.D., and Zegzhda, D.P., Ensuring the safety of automated process control systems in hydropower facilities, Izv. Ross. Akad. Nauk, Ser. Energ., 2016, no. 3.
  2. Lavrova, D.S., An approach to developing the SIEM system for the Internet of Things, Autom. Control Comp. Sci., 2016, vol. 50, p. 673. https://doi.org/10.3103/S0146411616080125
    Article Google Scholar
  3. Tulone, D. and Madden, S., PAQ: Time series forecasting for approximate query answering in sensor networks, in European Workshop on Wireless Sensor Networks, Berlin, Heidelberg: Springer, 2006, pp. 21–37.
  4. Wei, L., et al., Assumption-free anomaly detection in time series, SSDBM, 2005, vol. 5, pp. 237–242.
    Google Scholar
  5. Pincombe, B., Anomaly detection in time series of graphs using arma processes, Asor Bull., 2005, vol. 24, no. 4, p. 2.
    Google Scholar
  6. Hundman, K., et al., Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding, arXiv preprint arXiv:1802.04431, 2018.
  7. Filonov, P., Lavrentyev, A., and Vorontsov, A., Multivariate industrial time series with cyber-attack simulation: Fault detection using an lstm-based predictive data model, arXiv preprint arXiv:1612.06676, 2016.
  8. Nanduri, A. and Sherry, L., Anomaly detection in aircraft data using Recurrent Neural Networks (RNN), Integrated Communications Navigation and Surveillance (ICNS), 2016, pp. 5C2-1–5C2-8.
  9. Yi, S., et al., Grouped convolutional neural networks for multivariate time series, arXiv preprint arXiv:1703.09938, 2017.
  10. Supercomputer Center Polytechnic. https://www.scc.spbstu.ru.

Download references

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).

Author information

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
  2. D. S. Lavrova
  3. A. V. Yarmak

Corresponding authors

Correspondence toM. O. Kalinin or D. S. Lavrova.

Additional information

Translated by S. Avodkova

About this article

Cite this article

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

Download citation

Keywords: