Digital Cryptography Implementation using Neurocomputational Model with Autoencoder Architecture

Francisco Quinga Socasi, Ronny Velastegui, Luis Zhinin-Vera, Rafael Valencia-Ramos, Francisco Quinga Socasi, Oscar Chang

2020

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 100% in the processes to encrypt 52 ASCII characters (Letter characters) and 95 ASCII characters (printable characters), recovering all the data.

Paper Citation

in Harvard Style

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, pages 865-872. DOI: 10.5220/0009154908650872

in Bibtex Style

@conference{icaart20,
author={Francisco Socasi and Ronny Velastegui and Luis Zhinin-Vera and Rafael Valencia-Ramos and Francisco 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},
}

in EndNote Style

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
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