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Network Traffic Anomaly Detection in Railway Intelligent Control Systems Using Nonlinear Dynamics Approach

  • Maria A. Butakova
  • Andrey V. Chernov
  • Sergey M. Kovalev
  • Andrey V. SukhanovEmail author
  • Stanislav Zajaczek
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 554)

Abstract

The work presents an approach for anomaly detection in network traffic based on nonlinear dynamics techniques. The main attention is paid to nonlinear-dynamical models of telecommunication traffic in the distributed network subsystems of railway intelligent control systems. In the considered system, telecommunication traffic is presented in time series form. The time series is used as the basis for reconstructed nonlinear dynamic system with chaotic behavior. The calculation algorithms for embedding dimension, correlation dimension and spectrum of Lyapunov exponents are given. The computational implementations for assessment of dynamical characteristics reconstructed from noisy time series of network traffic are presented. Anomaly detection algorithm based on Lyapunov exponents calculation is presented for nonlinear system generating network traffic.

Keywords

Anomaly detection Network traffic Nonlinear dynamics Lyapunov exponents Railway intelligent control system 

Notes

Acknowledgement

The work was supported Grant No. SP2018/163 “Diagnostics, reliability and efficiency of electrical machines and devices, problems of antenna systems” and by Russian Foundation for Basic Research (Grants No. 16-07-00888-a, 18-08-00549-a, 17-20-01040 ofi_m_RZD, No. 16-07-00032-a and No. 16-07-00086-a).

References

  1. 1.
    Chernov, A.V., Butakova, M.A., Karpenko, E.V.: Security incident detection technique for multilevel intelligent control systems on railway transport in Russia. In: 2015 23rd Telecommunications Forum Telfor (TELFOR), pp. 1–4. IEEE (2015)Google Scholar
  2. 2.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection for discrete sequences: a survey. IEEE Trans. Knowl. Data Eng. 24(5), 823–839 (2012)CrossRefGoogle Scholar
  3. 3.
    Chemov, A.V., Butakova, M.A., Karpenko, E.V., Kartashov, O.O.: Improving security incidents detection for networked multilevel intelligent control systems in railway transport. Telfor J. 8(1), 14–19 (2016)CrossRefGoogle Scholar
  4. 4.
    Butakova, M.A., Chernov, A.V., Shevchuk, P.S., Vereskun, V.D.: Complex event processing for network anomaly detection in digital railway communication services. In: 2017 25th Telecommunication Forum (TELFOR), pp. 1–4. IEEE (2017)Google Scholar
  5. 5.
    Akimaru, H., Kawashima, K.: Teletraffic: Theory and Applications. Springer Science & Business Media, New York (2012)Google Scholar
  6. 6.
    Sheluhin, O., Smolskiy, S., Osin, A.: Self-similar processes in telecommunications. John Wiley & Sons, New York (2007)CrossRefGoogle Scholar
  7. 7.
    Fu, C., Jiang, H.Y.: On the chaotic dynamics analysis of internet traffic. In: 2008 International Conference on Intelligent Computation Technology and Automation, pp. 840–844. IEEE (2008)Google Scholar
  8. 8.
    Guo, X., Vogel, D., Zhou, Z., Zhang, X., Chen, H.: Chaos theory as a model for interpreting weblog traffic. In: Proceedings of the 41st Annual Hawaii International Conference on System Sciences, p. 289. IEEE (2008)Google Scholar
  9. 9.
    Kantz, H., Schreiber, T.: Nonlinear time series analysis, vol. 7. Cambridge University Press, New York (2004)zbMATHGoogle Scholar
  10. 10.
    Takens, F.: Detecting strange attractors in turbulence. In: Dynamical systems and turbulence, Warwick 1980, pp. 366–381. Springer (1981)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Maria A. Butakova
    • 1
  • Andrey V. Chernov
    • 1
  • Sergey M. Kovalev
    • 1
  • Andrey V. Sukhanov
    • 1
    • 2
    Email author
  • Stanislav Zajaczek
    • 2
  1. 1.Rostov State Transport UniversityRostov-on-DonRussia
  2. 2.VSB - Technical University of OstravaOstravaCzech Republic

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