Cyberattack intensity forecasting on informatization objects of critical infrastructures

Content Provider	Scilit
Author	Krakovsky, Y. M. Luzgin, A. N. Ivanyo, Y. M.
Copyright Year	2019
Description	Journal: Iop Conference Series: Materials Science and Engineering In regulatory documents of recent years in the field of information security, much attention is paid to information systems of critical infrastructures. This, in turn, justifies the need for scientific research on the development of new methods of protection against cyberattacks on such information systems. For this task, interval forecasting is recommended based on a probabilistic neural network with dynamic updating of the smoothing parameter. As benchmarks for comparing the interval forecasting results, the naive Bayesian model and the probabilistic cluster model were chosen.
Related Links	https://iopscience.iop.org/article/10.1088/1757-899X/481/1/012003/pdf
ISSN	17578981
e-ISSN	1757899X
DOI	10.1088/1757-899x/481/1/012003
Journal	Iop Conference Series: Materials Science and Engineering
Issue Number	1
Volume Number	481
Language	English
Publisher	IOP Publishing
Publisher Date	2019-03-11
Access Restriction	Open
Subject Keyword	Journal: Iop Conference Series: Materials Science and Engineering Information Systems
Content Type	Text
Resource Type	Article