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Automatic Control and Computer Sciences

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01-12-2020

Identification of Anomalies in the Operation of Telecommunication Devices Based on Local Signal Spectra

Authors: M. E. Sukhoparov, V. V. Semenov, K. I. Salakhutdinova, I. S. Lebedev

Published in: Automatic Control and Computer Sciences | Issue 8/2020

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Abstract—

Telecommunication devices are becoming one of the critical elements of industrial systems. They are an attractive target for potential attackers. A method for identification of anomalies based on local signal spectra and using neural networks for evaluation is considered. An experiment is performed on the basis of statistical data on the loading of a computing device.

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Vasiliev, Y.S., Zegzhda, P.D., and Kuvshinov, V.I., Modern problems of cybersecurity, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2014, vol. 17, no. 3, pp. 210–214.

Anisimov, V.G., Anisimov, E.G., Zegzhda, P.D., and Suprun, A.F., The problem of innovative development of information security systems in the transport sector, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 1105–1110.CrossRef

Pavlenko, E. and Zegzhda, D., Sustainability of cyber-physical systems in the context of targeted destructive influences, IEEE Industrial Cyber-Physical Systems, ICPS 2018, 2018, pp. 830–834.

Zegzhda, D.P., Poltavtseva, M.A., and Lavrova, D.S., Systematization and security assessment of cyber-physical systems, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 835–843.CrossRef

Belenko, V., Krundyshev, V., and Kalinin, M., Synthetic datasets generation for intrusion detection in VANET, ACM International Conference Proceeding Series, 2018. https://doi.org/10.1145/3264437.3264479

Poltavtseva, M.A., Zegzhda, D.P., and Pavlenko, E.Y., High-performance NIDS architecture for enterprise networking, 2019 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2019, 2019. https://doi.org/10.1109/BlackSeaCom.2019.8812808

Platonov, V.V. and Semenov, P.O., Using data-mining methods to detect network attacks, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 766–769.CrossRef

Zegzhda, D.P. and Kalinin, M.O., Security assurance for the attribute-based access control systems, International Conference on Enterprise Information Systems and Web Technologies 2008, EISWT 2008, pp. 147–154.

Krundyshev, V. and Kalinin, M., Prevention of false data injections in smart infrastructures, 2019 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2019, 2019. https://doi.org/10.1109/BlackSeaCom.2019.8812786

10.

Demidov, R.A., Zegzhda, P.D., and Kalinin, M.O., Threat analysis of cyber security in wireless adhoc networks using hybrid neural network model, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 971–976.CrossRef

11.

Kalinin, M., Krundyshev, V., Rezedinova, E., and Zegzhda, P., Role-based access control for vehicular adhoc networks, 2018 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2018, 2018. https://doi.org/10.1109/BlackSeaCom.2018.8433628

12.

Farwell, J.P. and Rohozinski, R., Stuxnet and the future of cyber war, Survival, 2011, vol. 53, no. 1, pp. 23–40. https://doi.org/10.1080/00396338.2011.555586CrossRef

13.

Yeung, D.Y. and Ding, Y., Host-based intrusion detection using dynamic and static behavioral models, Pattern Recognit., 2003, vol. 36, no. 1, pp. 229–243.CrossRef

14.

Yuan, K., Ling, Q., and Yin, W., On the convergence of decentralized gradient descent, SIAM J. Optim., 2016, vol. 26, no. 3, pp. 1835–1854.MathSciNetCrossRef

15.

Slay, J. and Miller, M., Lessons learned from the Maroochy water breach, in Critical Infrastructure Protection, Springer, 2007, pp. 73–82.

16.

Zegzhda, D.P., Kalinin, M.O., and Levykin, M.V., Actual vulnerabilities of industrial automation protocols of an open platform communications series, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 972–979.CrossRef

17.

Gao, D., Reiter, M.K., and Song, D., Beyond output voting: Detecting compromised replicas using hmm-based behavioral distance, IEEE Trans. Dependable Secure Comput., 2009, vol. 6, no. 2, pp. 96–110.CrossRef

18.

Igure, V.M., Laughter, S.A., and Williams, R.D., Security issues in SCADA networks, Comput. Secur., 2006, vol. 25, no. 7, pp. 498–506.CrossRef

19.

Lavrova, D., Zegzhda, D., and Yarmak, A., Predicting cyber attacks on industrial systems using the Kalman filter, 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019, 2019, pp. 317–321. https://doi.org/10.1109/WorldS4.2019.8904038

20.

Zegzhda, P.D., Lavrova, D.S., and Shtyrkina, A.A., Multifractal analysis of Internet backbone traffic for detecting denial of service attacks, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 936–944.CrossRef

21.

Kalinin, M.O., Lavrova, D.S., and Yarmak, A.V., Detection of threats in cyberphysical systems based on deep learning methods using multidimensional time series, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 912–917.CrossRef

22.

Belenko, V., Krundyshev, V., and Kalinin, M., Intrusion detection for Internet of Things applying metagenome fast analysis, 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019, 2019, pp. 129–135. https://doi.org/10.1109/WorldS4.2019.8904022

23.

Zegzhda, P.D., Zegzhda, D.P., Kalinin, M.O., and Konoplev, A.S., Security modeling of grid systems using petri nets, Lect. Notes Comput. Sci., 2012, vol. 7531, pp. 299–308.CrossRef

24.

Kalinin, M., Demidov, R., and Zegzhda, P., Hybrid neural network model for protection of dynamic cyber infrastructure, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 4, pp. 375–382.

25.

26.

Kalinin, M.O., Zubkov, E.A., Suprun, A.F., and Pechenkin, A.I., Prevention of attacks on dynamic routing in self-organizing adhoc networks using swarm intelligence, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 977–983.CrossRef

27.

Krundyshev, V., Kalinin, M., and Zegzhda, P., Artificial swarm algorithm for VANET protection against routing attacks, 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018, 2018, pp. 795–800. https://doi.org/10.1109/ICPHYS.2018.8390808

28.

Malyshev, E.V., Moskvin, D.A., and Zegzhda, D.P., Application of an artificial neural network for detection of attacks in vanets, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 889–894.CrossRef

29.

Krundyshev, V. and Kalinin, M., Hybrid neural network frame work for detection of cyber attacks at smart infrastructures, ACM International Conference Proceeding Series, 2019. https://doi.org/10.1145/3357613.3357623

30.

Belenko, V., Chernenko, V., Kalinin, M., and Krundyshev, V., Evaluation of GAN applicability for intrusion detection in self-organizing networks of cyber physical systems, 2018 International Russian Automation Conference, RusAutoCon, 2018. https://doi.org/10.1109/RUSAUTOCON.2018.8501783

31.

Gers, F.A., Schmidhuber, J., and Cummins, F., Learning to forget: Continual prediction with LSTM, Neural Comput., 2000, vol. 12, no. 10, pp. 2451–2471.CrossRef

32.

Ho, T.K., Random decision forests in document analysis and recognition, 3rd International Conference on Document Analysis and Recognition, 1995, vol. 1, pp. 278–282.

33.

Stepanova, T., Kalinin, M., Baranov, P., and Zegzhda, D., Homogeneity analysis of power consumption for information security purposes, 3rd International Conference of Security of Information and Networks, 2010, pp. 113–117. https://doi.org/10.1145/1854099.1854124

34.

Semenov, V.V., Lebedev, I.S., and Sukhoparov, M.E., An approach to the classification of the state of information security of elements of cyber-physical systems using collateral electromagnetic radiation, Nauchno-Tekh. Vestn. Inf. Mekh. Opt., 2018, vol. 18, no. 1, pp. 98–105. https://doi.org/10.17586/2226-1494-2018-18-1-98-105CrossRef

35.

Semenov, V.V., Application of an autonomous object behavior model to classify the cybersecurity state, Internet of Things, Smart Spaces, and Next Generation Networks and Systems, 2009, pp. 104–112.

36.

Bendat, J.S. and Piersol, A.G., Engineering Applications of Correlation and Spectral Analysis, Wiley, 1980.MATH

Title: Identification of Anomalies in the Operation of Telecommunication Devices Based on Local Signal Spectra
Authors: M. E. Sukhoparov
V. V. Semenov
K. I. Salakhutdinova
I. S. Lebedev
Publication date: 01-12-2020
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 8/2020
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411620080337

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