Camilo Ravelo

May 21, 2024 | mdpi.com | Rodrigo Olivares |Omar Salinas |Camilo Ravelo |Ricardo Soto

1. IntroductionIn the digital age, the landscape of the contemporary world is increasingly shaped by technological advancements, where threats in the cyber realm pose significant challenges to enterprises. Recognizing these threats necessitates a nuanced understanding of cybersecurity culture, the development of robust cyber-risk management strategies, and the adoption of a proactive, collaborative approach tailored to each organization’s unique context [1,2].

Apr 20, 2024 | mdpi.com | Rodrigo Olivares |Camilo Ravelo |Ricardo Soto |Broderick Crawford

All articles published by MDPI are made immediately available worldwide under an open access license. No specialpermission is required to reuse all or part of the article published by MDPI, including figures and tables. Forarticles published under an open access Creative Common CC BY license, any part of the article may be reused withoutpermission provided that the original article is clearly cited. For more information, please refer tohttps://www.mdpi.com/openaccess.

Apr 12, 2024 | preprints.org | Rodrigo Olivares |Omar Salinas |Camilo Ravelo |Ricardo Soto

Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 11 April 2024 / Approved: 11 April 2024 / Online: 12 April 2024 (08:28:04 CEST) Olivares, R.; Salinas, O.; Ravelo, C.; Soto, R.; Crawford, B. Enhancing the Efficiency of a Cyber SOC Using Biomimetic Algorithms Empowered by Deep Q–Learning. Preprints 2024, 2024040832. https://doi.org/10.20944/preprints202404.0832.v1 Olivares, R.; Salinas, O.; Ravelo, C.; Soto, R.; Crawford, B.

Mar 26, 2024 | preprints.org | Rodrigo Olivares |Ricardo Soto |Broderick Crawford |Camilo Ravelo

Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 25 March 2024 / Approved: 26 March 2024 / Online: 26 March 2024 (07:27:45 CET) Olivares, R.; Ravelo, C.; Soto, R.; Crawford, B. Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection. Preprints 2024, 2024031533. https://doi.org/10.20944/preprints202403.1533.v1 Olivares, R.; Ravelo, C.; Soto, R.; Crawford, B.