Leveraging improved approaches for the investigation of patterns and randomness in digital chaos
Autor(a) principal: | |
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Data de Publicação: | 2024 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/76/76132/tde-26082024-085906/ |
Resumo: | Dynamical deterministic systems with chaotic properties have been actively studied and new applications are established as the qualities of these systems are tested and proved. The k-logistic map is a variation of the logistic map that presents interesting properties for applications in generating pseudo-random numbers (PRNGs) and encryption. Given that the orbits generated by the k-logistic map present characteristics of high entropy and uniform distribution under statistical tests, question arises on which transformations and projections on the orbit of the k-logistic map are able to reveal patterns that are imperceptible in the original space. And another question also arises about which combinations of computer-based and mathematics-based techniques and methods of the arsenal of mathematics should be included into the toolset. This proposal has applications in the areas of cryptoanalysis, dynamical systems analysis and pattern recognition. The following methods were employed: generation of orbits with the k-logistic map, statistical test suites, local density measurements, topological graphs on objects contained in metric spaces, Dynamic Time Warping, spectral analysis, random matrix theory and machine learning. When projecting an orbit or distributions of orbits in metric spaces, and constructing topological graph distributions from these projections, even simple and conventional statistical tests revealed previously imperceptible patterns that were considered to be mere random noises. The results that were found from this development have relevance in improving test batteries for number sequences and analysis of dynamical systems. |
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Leveraging improved approaches for the investigation of patterns and randomness in digital chaosAlavancando abordagens melhoradas para a investigação de padrões e aleatoriedade no caos digitalCaosChaosDynamical systemsPattern recognitionReconhecimento de padrõesSistemas dinâmicosDynamical deterministic systems with chaotic properties have been actively studied and new applications are established as the qualities of these systems are tested and proved. The k-logistic map is a variation of the logistic map that presents interesting properties for applications in generating pseudo-random numbers (PRNGs) and encryption. Given that the orbits generated by the k-logistic map present characteristics of high entropy and uniform distribution under statistical tests, question arises on which transformations and projections on the orbit of the k-logistic map are able to reveal patterns that are imperceptible in the original space. And another question also arises about which combinations of computer-based and mathematics-based techniques and methods of the arsenal of mathematics should be included into the toolset. This proposal has applications in the areas of cryptoanalysis, dynamical systems analysis and pattern recognition. The following methods were employed: generation of orbits with the k-logistic map, statistical test suites, local density measurements, topological graphs on objects contained in metric spaces, Dynamic Time Warping, spectral analysis, random matrix theory and machine learning. When projecting an orbit or distributions of orbits in metric spaces, and constructing topological graph distributions from these projections, even simple and conventional statistical tests revealed previously imperceptible patterns that were considered to be mere random noises. The results that were found from this development have relevance in improving test batteries for number sequences and analysis of dynamical systems.Sistemas dinâmicos determinísticos com propriedades caóticas vêm sendo estudados ativamente e novas aplicações são estabelecidas à medida em que as qualidades desses sistemas são postas à prova e aclamadas. O k-mapa logístico é uma variação do mapa logístico que apresenta propriedades interessantes para aplicações em geração de números pseudoaleatórios (PRNGs) e criptografia. Dado que as órbitas geradas pelo k-mapa logístico apresentam características de alta entropia e distribuição uniforme sob testes estatísticos, surge a questão sobre quais transformações e projeções sobre a órbita do k-mapa logístico são capazes de revelar padrões que são imperceptíveis no espaço de origem. E também surge a questão sobre quais combinações de técnicas e métodos do arsenal da matemática e da computação devem ser incluídos no ferramental. A presente proposta possui aplicação nas áreas de criptoanálise, análise de sistemas dinâmicos e reconhecimento de padrões. Os seguintes métodos foram empregados: geração de órbitas com o k-mapa logístico, suítes de testes estatísticos, medidas de densidade local, grafos topológicos sobre objetos contidos em espaços métricos, Dynamic Time Warping, análise espectral, teoria de matrizes aleatórias e aprendizado de máquina. Ao se projetar a órbita ou distribuições de órbitas em espaços métricos, e construindo-se distribuições de grafos topológicos a partir dessas projeções, mesmo testes estatísticos simples e convencionais revelaram padrões antes imperceptíveis e considerados como meros ruídos aleatórios. Os resultados encontrados a partir desse desenvolvimento possuem relevância no aperfeiçoamento de baterias de testes para sequências de números e análise de sistemas dinâmicos.Biblioteca Digitais de Teses e Dissertações da USPBruno, Odemir MartinezBispo Junior, Altamir Gomes2024-05-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/76/76132/tde-26082024-085906/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2024-08-29T19:00:02Zoai:teses.usp.br:tde-26082024-085906Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212024-08-29T19:00:02Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Leveraging improved approaches for the investigation of patterns and randomness in digital chaos Alavancando abordagens melhoradas para a investigação de padrões e aleatoriedade no caos digital |
title |
Leveraging improved approaches for the investigation of patterns and randomness in digital chaos |
spellingShingle |
Leveraging improved approaches for the investigation of patterns and randomness in digital chaos Bispo Junior, Altamir Gomes Caos Chaos Dynamical systems Pattern recognition Reconhecimento de padrões Sistemas dinâmicos |
title_short |
Leveraging improved approaches for the investigation of patterns and randomness in digital chaos |
title_full |
Leveraging improved approaches for the investigation of patterns and randomness in digital chaos |
title_fullStr |
Leveraging improved approaches for the investigation of patterns and randomness in digital chaos |
title_full_unstemmed |
Leveraging improved approaches for the investigation of patterns and randomness in digital chaos |
title_sort |
Leveraging improved approaches for the investigation of patterns and randomness in digital chaos |
author |
Bispo Junior, Altamir Gomes |
author_facet |
Bispo Junior, Altamir Gomes |
author_role |
author |
dc.contributor.none.fl_str_mv |
Bruno, Odemir Martinez |
dc.contributor.author.fl_str_mv |
Bispo Junior, Altamir Gomes |
dc.subject.por.fl_str_mv |
Caos Chaos Dynamical systems Pattern recognition Reconhecimento de padrões Sistemas dinâmicos |
topic |
Caos Chaos Dynamical systems Pattern recognition Reconhecimento de padrões Sistemas dinâmicos |
description |
Dynamical deterministic systems with chaotic properties have been actively studied and new applications are established as the qualities of these systems are tested and proved. The k-logistic map is a variation of the logistic map that presents interesting properties for applications in generating pseudo-random numbers (PRNGs) and encryption. Given that the orbits generated by the k-logistic map present characteristics of high entropy and uniform distribution under statistical tests, question arises on which transformations and projections on the orbit of the k-logistic map are able to reveal patterns that are imperceptible in the original space. And another question also arises about which combinations of computer-based and mathematics-based techniques and methods of the arsenal of mathematics should be included into the toolset. This proposal has applications in the areas of cryptoanalysis, dynamical systems analysis and pattern recognition. The following methods were employed: generation of orbits with the k-logistic map, statistical test suites, local density measurements, topological graphs on objects contained in metric spaces, Dynamic Time Warping, spectral analysis, random matrix theory and machine learning. When projecting an orbit or distributions of orbits in metric spaces, and constructing topological graph distributions from these projections, even simple and conventional statistical tests revealed previously imperceptible patterns that were considered to be mere random noises. The results that were found from this development have relevance in improving test batteries for number sequences and analysis of dynamical systems. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-05-06 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/76/76132/tde-26082024-085906/ |
url |
https://www.teses.usp.br/teses/disponiveis/76/76132/tde-26082024-085906/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
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1815257400497668096 |