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Chaotic cellular neural network‐based true random number generator
Author(s) -
Karakaya Barış,
Çelik Vedat,
Gülten Arif
Publication year - 2017
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.2374
Subject(s) - nist , chaotic , random number generation , field programmable gate array , computer science , series (stratigraphy) , generator (circuit theory) , artificial neural network , entropy (arrow of time) , algorithm , cellular neural network , key (lock) , artificial intelligence , computer hardware , speech recognition , power (physics) , paleontology , physics , computer security , quantum mechanics , biology
Summary This paper presents implementation of a chaotic cellular neural network (CNN)‐based true random number generator on a field programmable gate array (FPGA) board. In this implementation, discrete time model of the chaotic CNN is used as the entropy source. Random number series are generated for three scenarios. Obtained number series are tested by using NIST 800.22 statistical test suite. Also, the scale index technique is carried out for these three scenarios to determine the degree of non‐periodicity for key stream. Copyright © 2017 John Wiley & Sons, Ltd.