Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks

Energies

Daniil Titov¹  Andrey Privalov¹  Igor Saenko²  Igor Kotenko²  Natalya Evglevskaya³ 

[1] Electrical Communication Department, Emperor Alexander I Saint-Petersburg State Transport University,9 Moskovsky pr., St. Petersburg 190031, Russia;Saint-Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) 39, 14 Liniya, 199178 St. Petersburg, Russia;St. Petersburg Signal Academy, 3 Tikhoretsky Ave., 194064 St. Petersburg, Russia;

关键词: cyberattack;    data transmission network;    stochastic network;    equivalent function;    Laplace–Stieltjes transform;    distribution function;   

DOI  :  10.3390/en14164755

来源: DOAJ

【 摘 要 】

Cyberattacks against the elements of technological data transmission networks represent a rather significant threat of disrupting the management of regional electric power complexes. Therefore, evaluating the functioning quality of data transmission networks in the context of cyberattacks is an important task that helps to make the right decisions on the telecommunication support of electric power systems. The known models and methods for solving this problem have limited application areas determined by the admissible packet distribution laws. The paper proposes a new method for evaluating the quality of the functioning of data transmission networks, based on modeling the process of functioning of data transmission networks in the form of a stochastic network. The proposed method removes restrictions on the form of the initial distributions and makes the assumptions about the exponential distribution of the expected time and packet servicing in modern technological data transmission networks unnecessary. The method gives the possibility to evaluate the quality of the network functioning in the context of cyberattacks for stationary Poisson transmission and self-similar traffic, represented by Pareto and Weibul flows models. The obtained evaluation results are in good agreement with the data represented in previously published papers.

【 授权许可】

Unknown