An Approach to Detecting Cyber Attacks against Smart Power Grids Based on the Analysis of Network Traffic Self-Similarity

Igor Kotenko, Igor Saenko, Oleg Lauta and Aleksander Kribel
Additional contact information
Igor Kotenko: St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, 199178 St. Petersburg, Russia
Igor Saenko: St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), 39, 14 Liniya, 199178 St. Petersburg, Russia
Oleg Lauta: Admiral Makarov State University of Maritime and Inland Shipping, 5/7 Dvinskaya st., 198035 St. Petersburg, Russia
Aleksander Kribel: Saint-Petersburg Signal Academy, 3 Tikhoretsky av., 194064 St. Petersburg, Russia

Energies, 2020, vol. 13, issue 19, 1-24

Abstract: The paper discusses an approach for detecting cyber attacks against smart power supply networks, based on identifying anomalies in network traffic by assessing its self-similarity property. Methods for identifying long-term dependence in fractal Brownian motion and real network traffic of smart grid systems are considered. It is shown that the traffic of a telecommunication network is a self-similar structure, and its behavior is close to fractal Brownian motion. Fractal analysis and mathematical statistics are used as tools in the development of this approach. The issues of a software implementation of the proposed approach and the formation of a dataset containing network packets of smart grid systems are considered. The experimental results obtained using the generated dataset have demonstrated the existence of self-similarity in the network traffic of smart grid systems and confirmed the fair efficiency of the proposed approach. The proposed approach can be used to quickly detect the presence of anomalies in the traffic with the aim of further using other methods of cyber attack detection.

Keywords: cyber security; smart grid; anomaly detection; cyber attacks; time series; fractal analysis; Hurst metric; scaling metric (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/19/5031/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/19/5031/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:19:p:5031-:d:418721

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().