| Issue |
SHS Web of Conf.
Volume 44, 2018
IV International Scientific Conference “The Convergence of Digital and Physical Worlds: Technological, Economic and Social Challenges” (CC-TESC2018)
|
|
|---|---|---|
| Article Number | 00052 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/shsconf/20184400052 | |
| Published online | 05 June 2018 | |
Wavelet-analysis of network traffic time-series for detection of attacks on digital production infrastructurea
Peter the Great Saint Petersburg Polytechnic University, Institute of Applied Mathematics and Mechanics, 195251 Polytechnicheskaya st. 29, Russian Federation
* Corresponding author: lavrova@ibks.spbstu.ru
Digital production integrates with all the areas of human activity including critical industries, therefore the task of detecting network attacks has a key priority in protecting digital manufacture systems. This article offers an approach for analysis of digital production security based on evaluation of a posteriori probability for change point in time-series, which are based on the change point coefficient values of digital wavelet-transform in the network traffic time-series. These time-series make it possible to consider the network traffic from several points of view at the same time, which plays an important role in the task of detecting network attacks. The attack methods vary significantly; therefore, in order to detect them it is necessary to monitor different values of various traffic parameters. The proposed method has demonstrated its efficiency in detecting network service denial attacks (SlowLoris and HTTP DoS) being realized at the application level.
With financial support from the Ministry of education and science of the Russian Federation as part of the Federal target program “Research and development of priority areas for Russia’s research and process complex for 2014-2020” (Agreement No.14.578.21.0231, unique identifier of this agreement RFMEFI57817X0231)
© 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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