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Authors: Francisco Quinga Socasi ; Ronny Velastegui ; Luis Zhinin-Vera ; Rafael Valencia-Ramos ; Francisco Quinga Ortega-Zamorano and Oscar Chang

Affiliation: School of Mathematical and Computational Sciences, Yachay Tech University, 100650, Urcuqui, Ecuador

Keyword(s): Cryptography, Artificial Neural Network, Autoencoder, ASCII Characters.

Abstract: An Autoencoder is an artificial neural network used for unsupervised learning and for dimensionality reduction. In this work, an Autoencoder has been used to encrypt and decrypt digital information. So, it is implemented to code and decode characters represented in an 8-bit format, which corresponds to the size of ASCII representation. The Back-propagation algorithm has been used in order to perform the learning process with two different variant depends on when the discretization procedure is carried out, during (model I) or after (model II) the learning phase. Several tests were conducted to determine the best Autoencoder architectures to encrypt and decrypt, taking into account that a good encrypt method corresponds to a process that generate a new code with uniqueness and a good decrypt method successfully recovers the input data. A network that obtains a 100% in the two process is considered a good digital cryptography implementation. Some of the proposed architecture obtain a 1 00% in the processes to encrypt 52 ASCII characters (Letter characters) and 95 ASCII characters (printable characters), recovering all the data. (More)

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Paper citation in several formats:
Socasi, F.; Velastegui, R.; Zhinin-Vera, L.; Valencia-Ramos, R.; Ortega-Zamorano, F. and Chang, O. (2020). Digital Cryptography Implementation using Neurocomputational Model with Autoencoder Architecture. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 865-872. DOI: 10.5220/0009154908650872

@conference{icaart20,
author={Francisco Quinga Socasi. and Ronny Velastegui. and Luis Zhinin{-}Vera. and Rafael Valencia{-}Ramos. and Francisco Quinga Ortega{-}Zamorano. and Oscar Chang.},
title={Digital Cryptography Implementation using Neurocomputational Model with Autoencoder Architecture},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2020},
pages={865-872},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009154908650872},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Digital Cryptography Implementation using Neurocomputational Model with Autoencoder Architecture
SN - 978-989-758-395-7
IS - 2184-433X
AU - Socasi, F.
AU - Velastegui, R.
AU - Zhinin-Vera, L.
AU - Valencia-Ramos, R.
AU - Ortega-Zamorano, F.
AU - Chang, O.
PY - 2020
SP - 865
EP - 872
DO - 10.5220/0009154908650872
PB - SciTePress

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