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Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry
As the likelihood and impact of cyber-attacks continue to grow, organizations realize the need to invest in specialized methods to protect their digital data and the information they circulate or manage. Due to its broad use, game theory has evolved into a concept that can be applied practically whi...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Hindawi
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232310/ https://www.ncbi.nlm.nih.gov/pubmed/35755750 http://dx.doi.org/10.1155/2022/2266171 |
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author | Jing, Jing |
author_facet | Jing, Jing |
author_sort | Jing, Jing |
collection | PubMed |
description | As the likelihood and impact of cyber-attacks continue to grow, organizations realize the need to invest in specialized methods to protect their digital data and the information they circulate or manage. Due to its broad use, game theory has evolved into a concept that can be applied practically while analyzing and modifying existing cyber protection methods to arrive at the best possible conclusions. This study presents an innovative hybrid model that combines game theory and advanced machine learning methods for adaptive cyber defense strategies. Specifically, a repetitive game methodology is implemented to analyze cyber-attacks and model behaviors and study how defenders and attackers make decisions in a competing field. Based on Bayesian inference, the proposed method can predict the next steps in the game to produce the appropriate countermeasures and implement the best cyber defense strategies that govern an organization. The suggested system introduced to the academic literature for the first time was successfully tested in a particular application scenario involving the digital music industry and coping with impending cyber-attacks. |
format | Online Article Text |
id | pubmed-9232310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92323102022-06-25 Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry Jing, Jing Comput Intell Neurosci Research Article As the likelihood and impact of cyber-attacks continue to grow, organizations realize the need to invest in specialized methods to protect their digital data and the information they circulate or manage. Due to its broad use, game theory has evolved into a concept that can be applied practically while analyzing and modifying existing cyber protection methods to arrive at the best possible conclusions. This study presents an innovative hybrid model that combines game theory and advanced machine learning methods for adaptive cyber defense strategies. Specifically, a repetitive game methodology is implemented to analyze cyber-attacks and model behaviors and study how defenders and attackers make decisions in a competing field. Based on Bayesian inference, the proposed method can predict the next steps in the game to produce the appropriate countermeasures and implement the best cyber defense strategies that govern an organization. The suggested system introduced to the academic literature for the first time was successfully tested in a particular application scenario involving the digital music industry and coping with impending cyber-attacks. Hindawi 2022-06-17 /pmc/articles/PMC9232310/ /pubmed/35755750 http://dx.doi.org/10.1155/2022/2266171 Text en Copyright © 2022 Jing Jing. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Jing, Jing Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry |
title | Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry |
title_full | Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry |
title_fullStr | Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry |
title_full_unstemmed | Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry |
title_short | Applications of Game Theory and Advanced Machine Learning Methods for Adaptive Cyberdefense Strategies in the Digital Music Industry |
title_sort | applications of game theory and advanced machine learning methods for adaptive cyberdefense strategies in the digital music industry |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232310/ https://www.ncbi.nlm.nih.gov/pubmed/35755750 http://dx.doi.org/10.1155/2022/2266171 |
work_keys_str_mv | AT jingjing applicationsofgametheoryandadvancedmachinelearningmethodsforadaptivecyberdefensestrategiesinthedigitalmusicindustry |