loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Rafael Valencia-Ramos 1 ; 2 ; Luis Zhinin-Vera 1 ; 2 ; Gissela E. Pilliza 1 ; 2 and Oscar Chang 1 ; 2

Affiliations: 1 MIND Research Group - Model Intelligent Networks Development, Urcuqui, Ecuador ; 2 School of Mathematical and Computational Sciences, Yachay Tech University, 100650, Urcuqui, Ecuador

Keyword(s): Autoencoder, Cryptography, Cryptosystem, Encryption and Decryption Keys, Artificial Neural Networks.

Abstract: Protect the information has always been important concerns for society, and mainly now in digital era. Currently exists different platforms to manage critical and sensitive information, ranging from bank accounts to social media. All platforms have taken steps to guarantee that the data passing through them is protected from hackers. An essential subject in digital world born, giving place to symmetric and asymmetric key algorithms. Asymmetric key algorithms work by manipulating very big prime numbers, which gives a high level of security but also takes a long time to compute. This paper offers a cryptographic system based on deep learning techniques. The approach avoided the necessity of big prime numbers by using the synaptic weights of an autoencoder neural network as encryption and decryption keys. The suggested method allows for a high amount of unpredictability in the initial and final synaptic weights without compromising the network’s overall performance. The results was show n to be resilient and difficult to break in a theoretical security study with a low computational time. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.135.190.232

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Valencia-Ramos, R.; Zhinin-Vera, L.; Pilliza, G. and Chang, O. (2022). An Asymetric-key Cryptosystem based on Artificial Neural Network. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 540-547. DOI: 10.5220/0010857700003116

@conference{icaart22,
author={Rafael Valencia{-}Ramos. and Luis Zhinin{-}Vera. and Gissela E. Pilliza. and Oscar Chang.},
title={An Asymetric-key Cryptosystem based on Artificial Neural Network},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2022},
pages={540-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010857700003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - An Asymetric-key Cryptosystem based on Artificial Neural Network
SN - 978-989-758-547-0
IS - 2184-433X
AU - Valencia-Ramos, R.
AU - Zhinin-Vera, L.
AU - Pilliza, G.
AU - Chang, O.
PY - 2022
SP - 540
EP - 547
DO - 10.5220/0010857700003116
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic