Nardo, Lucas G., Nepomuceno, Erivelton, Bastos, Gustavo T., Santos, Thiago A., Butusov, Denis N. and Arias-Garcia, Janier (2021) A reliable chaos-based cryptography using Galois field. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31 (9). 091101-091101-9. ISSN 1054-1500
Preview
EN_a reliable.pdf
Download (6MB) | Preview
Abstract
Chaos-based image encryption schemes have been extensively employed over the past few years. Many issues such as the dynamical degradation of digital chaotic systems and information security have been explored, and plenty of successful solutions have also been proposed.
However, the impact of finite precision in different hardware and software setups has received little attention. In this work, we have shown
that the finite precision error may produce distinct cipher-images on different devices. In order to overcome this problem, we introduce an
efficient cryptosystem, in which the chaotic logistic map and the Galois field theory are applied. Our approach passes in the ENT test suite
and in several cyberattacks. It also presents an astonishing key space of up to 24096. Benchmark images have been effectively encrypted and
decrypted using dissimilar digital devices.
Item Type: | Article |
---|---|
Keywords: | reliable; chaos-based; cryptography; using; Galois field; |
Academic Unit: | Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: | 16820 |
Identification Number: | 10.1063/5.0061639 |
Depositing User: | Erivelton Nepomuceno |
Date Deposited: | 09 Jan 2023 14:42 |
Journal or Publication Title: | Chaos: An Interdisciplinary Journal of Nonlinear Science |
Publisher: | AIP Publishing |
Refereed: | Yes |
Related URLs: | |
URI: | https://mu.eprints-hosting.org/id/eprint/16820 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
Downloads
Downloads per month over past year