Probabilistic measure of space for neurocryptographic system solutions

Please use this identifier to cite or link to this item: https://elib.belstu.by/handle/123456789/31444

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DC Field	Value	Language
dc.contributor.author	Urbanovich, P.	en
dc.contributor.author	Karczmarski, D.	en
dc.contributor.author	Plonkowski, W.	en
dc.date.accessioned	2019-11-16T10:57:07Z	-
dc.date.available	2019-11-16T10:57:07Z	-
dc.date.issued	2019	-
dc.identifier.citation	Urbanovich, P. Probabilistic measure of space for neurocryptographic system solutions / P. Urbanovich, D. Karczmarski, M. Plonkowski // Proc. of 11th Intern. Conf. NEET’2019, Zakopane, Poland, June 25 - 28, 2019. – Lublin University of Techn., 2019. – P. 32	en
dc.identifier.uri	https://elib.belstu.by/handle/123456789/31444	-
dc.description	The cryptographic key matching can be based on artificial neural network technologies. Kinzel-Kanter protocol uses neural networks (A and B) ability in mutual learning and generating common secret key. This idea was developed (in particular, see. In this report a probabilistic approach that combines network parameters and the time of their synchronization is analyzed.	en
dc.subject	neurocryptography	en
dc.subject	system solutions	en
dc.title	Probabilistic measure of space for neurocryptographic system solutions	en
dc.type	Article	en
Appears in Collections:	Статьи в зарубежных изданиях

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