SHS Web of Conf.
Volume 44, 2018IV International Scientific Conference “The Convergence of Digital and Physical Worlds: Technological, Economic and Social Challenges” (CC-TESC2018)
|Number of page(s)||7|
|Published online||05 June 2018|
Estimation of applicability of modern neural network methods for preventing cyberthreats to self-organizing network infrastructures of digital economy platformsa,,b
Peter the Great St.Petersburg Polytechnic University, Department of Information security of computer systems, 195251, Politechnicheskaya st., 29, Russian Federation
* Corresponding author: email@example.com
The problems of applying neural network methods for solving problems of preventing cyberthreats to flexible self-organizing network infrastructures of digital economy platforms: vehicle adhoc networks, wireless sensor networks, industrial IoT, “smart buildings” and “smart cities” are considered. The applicability of the classic perceptron neural network, recurrent, deep, LSTM neural networks and neural networks ensembles in the restricting conditions of fast training and big data processing are estimated. The use of neural networks with a complex architecture– recurrent and LSTM neural networks – is experimentally justified for building a system of intrusion detection for self-organizing network infrastructures.
With financial support from the Ministry of Education and Science of the Russian Federation within the framework of the Federal Special Purpose Program “Studies and projects in the priority fields of development of the scientific technological complex of Russia for 2014-2020” (Agreement 14.575.21.0131 of September 26, 2017, 09.2017, unique identifier RFMEFI57517X0131).
The results of the work were obtained with the use of computing resources of the super-computer center of Peter the Great St. Petersburg Polytechnic University – Super-Computer Center “Politekhnichesky” (http://www.spbstu.ru).
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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