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)||10|
|Published online||05 June 2018|
Detection of cyber threats to network infrastructure of digital production based on the methods of Big Data and multifractal analysis of traffica
Peter the Great St. Petersburg Polytechnic University, Institute of Computer Sciences and Technologies, 195251 Polytechnicheskaya st. 29, Russian Federation
* Corresponding author: email@example.com
The article offers an approach to analyzing data security of network infrastructure of digital production providing for contraction of network traffic size and detecting anomalies in the network traffic on the basis of multifractal analysis. The contraction of data size will be provided due to extraction of significant parameters from the network packets and dropping the rest data, as well as due to application of such Big Data method as aggregation. The experimental investigations on contracting data size on analyzing security have proven the operability and efficiency thereof. The method of contracting data size has demonstrated a possibility of traffic volume contraction from hundreds of Gbit to several Mbyte. The suggested approach to security analysis using the assessment of width of multifractional spectrum as a criterion of anomaly presence has detected both simulated attacks of denial of servicing SYN-flood and smurf. Thus, the suggested approach can be efficiently used for analyzing big volumes of dissimilar traffic of network infrastructure of digital production.
With financial support from the Ministry of Education and Science of the Russian Federation in the framework of the Federal targeted program “Investigations and developments in the priority field of development of Russian science and technology complex for 2014-2020”, Agreement No. 14.578.21.0231, agreement unique identifier 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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