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Automatic Control and Computer Sciences

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01-12-2019

Identifying Cyberthreats in Modern Industrial Systems by Means of Deep-Learning Networks

Author: V. M. Krundyshev

Published in: Automatic Control and Computer Sciences | Issue 8/2019

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Abstract

This article presents an approach to building a system for identifying cyberthreats in modern industrial systems (IIoT, VANET, WSN) by means of artificial intelligence and deep learning. The results of the tests conducted to assess the suggested approach for efficiency based on deep-learning networks are provided.

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Zegzhda, D.P. and Ivashko, A.M., Osnovy informatsionnoi bezopasnosti informatsionnykh sistem (Fundamentals of Information Security of Information Systems), Goryachaya liniya—Telekom, 2000.

Lavrova, D., Pechenkin, A., and Gluhov, V., Applying correlation analysis methods to control flow violation detection in the internet of things, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 735–740.CrossRef

Sisinni, E., et al., Industrial Internet of Things: Challenges, opportunities, and directions, IEEE Trans. Ind. Inf., 2018, vol. 14, no. 11, pp. 4724–4734.CrossRef

Singh, R. and Nand, P., Literature review of routing attacks in MANET, 2016 International Conference on Computing, Communication and Automation (ICCCA), Noida, 2016, pp. 525–530.

Belenko, V., Krundyshev, V., and Kalinin, M., Synthetic datasets generation for intrusion detection in VANET, Proceedings of the 11th International Conference on Security of Information and Networks, 2018.

Ovasapyan, T.D., Ivanov, D.V., and Zegzhda, D.P., Securing wsn networks using the trust model, Regional’naya informatika i informatsionnaya bezopasnost’ (Regional Informatics and Information Security), 2017, pp. 421–422.

Haykin, S.S., Neural Networks: A Comprehensive Foundation, Prentice Hall, 1999.MATH

Kalinin, M.O., Krundyshev, V.M., and Semianov, P.V., Architectures for building secure vehicular networks based on SDN technology, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 907–914.CrossRef

NS-3. http://www.nsnam.org/.

10.

Karmanov, M.L., Routing protocol for ad hoc networks, Vestn. Yuzhno-Ural. Gos. Univ., Ser.: Komp’yut. Tekhnol. Upr. Radioelektron., 2009, no. 10, pp. 47–51.

11.

Keras. https://keras.io/.

12.

Tensorflow. https://www.tensorflow.org/.

13.

Deeplearning4j. https://deeplearning4j.org/.

14.

Theano. http://www.deeplearning.net/software/theano/.

Title: Identifying Cyberthreats in Modern Industrial Systems by Means of Deep-Learning Networks
Author: V. M. Krundyshev
Publication date: 01-12-2019
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 8/2019
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S014641161908011X

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