Optimizing quantum machine learning for proactive cybersecurity

The evolution of cyberattacks requires a continuous race to implement increasingly sophisticated techniques that allow us to stay ahead of cybercriminals. Thus, a relevant inverse problem in cybersecurity involves determining underlying patterns or models of possible cyber threats based on observed...

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Detalles Bibliográficos
Autores: Caballero Gil, Pino Teresa, Rosa Remedios, Carlos
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de La Laguna (ULL)
Repositorio:RIULL. Repositorio Institucional de la Universidad de La Laguna
OAI Identifier:oai:riull.ull.es:915/41198
Acceso en línea:http://riull.ull.es/xmlui/handle/915/41198
Access Level:acceso abierto
Palabra clave:Machine Learning
Quantum Machine Learning
Cybersecurity
Phishing
Malware
Spam
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spelling Optimizing quantum machine learning for proactive cybersecurityCaballero Gil, Pino TeresaRosa Remedios, CarlosMachine LearningQuantum Machine LearningCybersecurityPhishingMalwareSpamThe evolution of cyberattacks requires a continuous race to implement increasingly sophisticated techniques that allow us to stay ahead of cybercriminals. Thus, a relevant inverse problem in cybersecurity involves determining underlying patterns or models of possible cyber threats based on observed data. In particular, the processing of massive data combined with the application of Machine Learning methods and other techniques derived from Artificial Intelligence have so far achieved very significant advances in preventing and mitigating the impact of many cyberattacks. Given that the keyword in cybersecurity is anticipation, this work explores the possibilities of quantum computing and, in particular, of Quantum Machine Learning to have, when the quantum computing era arrives, the most optimal parameterisations to put these models into practice. Although the application of quantum technologies in a real context may still seem distant, having studies to assess the future viability of Quantum Machine Learning to identify different types of cyberattacks may be a differential element when it comes to ensuring the cybersecurity of essential services. For this reason, this work aims to use several datasets of known problems in the field of cybersecurity to evaluate the most optimal parameterisations in some known Quantum Machine Learning models, comparing the results with those obtained using classical models. After analysing the results of this study, it can be concluded that Quantum Machine Learning techniques are promising in the context of cybersecurity, giving rise to future work on a wider range of cybersecurity datasets and Quantum Machine Learning algorithms.Ingeniería Informática y de SistemasGrupo CryptULL de investigación en Criptología202520252024info:eu-repo/semantics/articleapplication/pdfhttp://riull.ull.es/xmlui/handle/915/41198reponame:RIULL. Repositorio Institucional de la Universidad de La Lagunainstname:Universidad de La Laguna (ULL)InglésOptimization and Engineering, 2024Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ESoai:riull.ull.es:915/411982026-06-22T13:13:57Z
dc.title.none.fl_str_mv Optimizing quantum machine learning for proactive cybersecurity
title Optimizing quantum machine learning for proactive cybersecurity
spellingShingle Optimizing quantum machine learning for proactive cybersecurity
Caballero Gil, Pino Teresa
Machine Learning
Quantum Machine Learning
Cybersecurity
Phishing
Malware
Spam
title_short Optimizing quantum machine learning for proactive cybersecurity
title_full Optimizing quantum machine learning for proactive cybersecurity
title_fullStr Optimizing quantum machine learning for proactive cybersecurity
title_full_unstemmed Optimizing quantum machine learning for proactive cybersecurity
title_sort Optimizing quantum machine learning for proactive cybersecurity
dc.creator.none.fl_str_mv Caballero Gil, Pino Teresa
Rosa Remedios, Carlos
author Caballero Gil, Pino Teresa
author_facet Caballero Gil, Pino Teresa
Rosa Remedios, Carlos
author_role author
author2 Rosa Remedios, Carlos
author2_role author
dc.contributor.none.fl_str_mv Ingeniería Informática y de Sistemas
Grupo CryptULL de investigación en Criptología
dc.subject.none.fl_str_mv Machine Learning
Quantum Machine Learning
Cybersecurity
Phishing
Malware
Spam
topic Machine Learning
Quantum Machine Learning
Cybersecurity
Phishing
Malware
Spam
description The evolution of cyberattacks requires a continuous race to implement increasingly sophisticated techniques that allow us to stay ahead of cybercriminals. Thus, a relevant inverse problem in cybersecurity involves determining underlying patterns or models of possible cyber threats based on observed data. In particular, the processing of massive data combined with the application of Machine Learning methods and other techniques derived from Artificial Intelligence have so far achieved very significant advances in preventing and mitigating the impact of many cyberattacks. Given that the keyword in cybersecurity is anticipation, this work explores the possibilities of quantum computing and, in particular, of Quantum Machine Learning to have, when the quantum computing era arrives, the most optimal parameterisations to put these models into practice. Although the application of quantum technologies in a real context may still seem distant, having studies to assess the future viability of Quantum Machine Learning to identify different types of cyberattacks may be a differential element when it comes to ensuring the cybersecurity of essential services. For this reason, this work aims to use several datasets of known problems in the field of cybersecurity to evaluate the most optimal parameterisations in some known Quantum Machine Learning models, comparing the results with those obtained using classical models. After analysing the results of this study, it can be concluded that Quantum Machine Learning techniques are promising in the context of cybersecurity, giving rise to future work on a wider range of cybersecurity datasets and Quantum Machine Learning algorithms.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://riull.ull.es/xmlui/handle/915/41198
url http://riull.ull.es/xmlui/handle/915/41198
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Optimization and Engineering, 2024
dc.rights.none.fl_str_mv Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
rights_invalid_str_mv Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:RIULL. Repositorio Institucional de la Universidad de La Laguna
instname:Universidad de La Laguna (ULL)
instname_str Universidad de La Laguna (ULL)
reponame_str RIULL. Repositorio Institucional de la Universidad de La Laguna
collection RIULL. Repositorio Institucional de la Universidad de La Laguna
repository.name.fl_str_mv
repository.mail.fl_str_mv
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