Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning.
In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimet...
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Online Access: | https://doi.org/10.3390/biomimetics9060307 https://pubmed.ncbi.nlm.nih.gov/38921187 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11201477/ |
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ftpubmed:38921187 2024-09-09T20:02:21+00:00 Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning. Olivares, Rodrigo Salinas, Omar Ravelo, Camilo Soto, Ricardo Crawford, Broderick 2024 May 21 https://doi.org/10.3390/biomimetics9060307 https://pubmed.ncbi.nlm.nih.gov/38921187 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11201477/ eng eng MDPI https://doi.org/10.3390/biomimetics9060307 https://pubmed.ncbi.nlm.nih.gov/38921187 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11201477/ Biomimetics (Basel) ISSN:2313-7673 Volume:9 Issue:6 biomimetic optimization algorithm cyber SOC deep Q-learning security information event management Journal Article 2024 ftpubmed https://doi.org/10.3390/biomimetics9060307 2024-06-28T16:02:00Z In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments. Article in Journal/Newspaper Orca PubMed Central (PMC) Biomimetics 9 6 307 |
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topic |
biomimetic optimization algorithm cyber SOC deep Q-learning security information event management |
spellingShingle |
biomimetic optimization algorithm cyber SOC deep Q-learning security information event management Olivares, Rodrigo Salinas, Omar Ravelo, Camilo Soto, Ricardo Crawford, Broderick Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning. |
topic_facet |
biomimetic optimization algorithm cyber SOC deep Q-learning security information event management |
description |
In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments. |
format |
Article in Journal/Newspaper |
author |
Olivares, Rodrigo Salinas, Omar Ravelo, Camilo Soto, Ricardo Crawford, Broderick |
author_facet |
Olivares, Rodrigo Salinas, Omar Ravelo, Camilo Soto, Ricardo Crawford, Broderick |
author_sort |
Olivares, Rodrigo |
title |
Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning. |
title_short |
Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning. |
title_full |
Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning. |
title_fullStr |
Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning. |
title_full_unstemmed |
Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning. |
title_sort |
enhancing the efficiency of a cybersecurity operations center using biomimetic algorithms empowered by deep q-learning. |
publisher |
MDPI |
publishDate |
2024 |
url |
https://doi.org/10.3390/biomimetics9060307 https://pubmed.ncbi.nlm.nih.gov/38921187 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11201477/ |
genre |
Orca |
genre_facet |
Orca |
op_source |
Biomimetics (Basel) ISSN:2313-7673 Volume:9 Issue:6 |
op_relation |
https://doi.org/10.3390/biomimetics9060307 https://pubmed.ncbi.nlm.nih.gov/38921187 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11201477/ |
op_doi |
https://doi.org/10.3390/biomimetics9060307 |
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Biomimetics |
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307 |
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1809934316169330688 |