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A Modular Dynamical Cryptosystem Based on Continuous-Interval Cellular Automata

A Modular Dynamical Cryptosystem Based on Continuous-Interval Cellular Automata

Jesus D. Terrazas Gonzalez, Witold Kinsner
Copyright: © 2011 |Volume: 5 |Issue: 4 |Pages: 27
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781613506028|DOI: 10.4018/jcini.2011100106
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MLA

Terrazas Gonzalez, Jesus D., and Witold Kinsner. "A Modular Dynamical Cryptosystem Based on Continuous-Interval Cellular Automata." IJCINI vol.5, no.4 2011: pp.83-109. http://doi.org/10.4018/jcini.2011100106

APA

Terrazas Gonzalez, J. D. & Kinsner, W. (2011). A Modular Dynamical Cryptosystem Based on Continuous-Interval Cellular Automata. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 5(4), 83-109. http://doi.org/10.4018/jcini.2011100106

Chicago

Terrazas Gonzalez, Jesus D., and Witold Kinsner. "A Modular Dynamical Cryptosystem Based on Continuous-Interval Cellular Automata," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 5, no.4: 83-109. http://doi.org/10.4018/jcini.2011100106

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Abstract

This paper presents a new cryptosystem based on chaotic continuous-interval cellular automata (CCA) to increase data protection as demonstrated by their flexibility to encrypt and decrypt information from distinct sources. Enhancements to cryptosystems are also presented including (i) a model based on a new chaotic CCA attractor, (ii) the dynamical integration of modules containing dynamical systems to generate complex sequences, and (iii) an enhancement for symmetric cryptosystems by allowing them to generate an unlimited number of keys. This paper also presents a process of mixing chaotic sequences obtained from cellular automata, instead of using differential equations, as a basis to achieve higher security and higher speed for the encryption and decryption processes, as compared to other recent approaches. The complexity of the mixed sequences is measured using the variance fractal dimension trajectory to compare them to the unmixed chaotic sequences to verify that the former are more complex. This type of polyscale measure and evaluation has never been done in the past outside this research group.

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