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Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation

Cybersecurity is a growing concern in today’s interconnected world. Traditional cybersecurity approaches, such as signature-based detection and rule-based firewalls, are often limited in their ability to effectively respond to evolving and sophisticated cyber threats. Reinforcement learning (RL) has...

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Autores principales: Oh, Sang Ho, Jeong, Min Ki, Kim, Hyung Chan, Park, Jongyoul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10051329/
https://www.ncbi.nlm.nih.gov/pubmed/36991711
http://dx.doi.org/10.3390/s23063000
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author Oh, Sang Ho
Jeong, Min Ki
Kim, Hyung Chan
Park, Jongyoul
author_facet Oh, Sang Ho
Jeong, Min Ki
Kim, Hyung Chan
Park, Jongyoul
author_sort Oh, Sang Ho
collection PubMed
description Cybersecurity is a growing concern in today’s interconnected world. Traditional cybersecurity approaches, such as signature-based detection and rule-based firewalls, are often limited in their ability to effectively respond to evolving and sophisticated cyber threats. Reinforcement learning (RL) has shown great potential in solving complex decision-making problems in various domains, including cybersecurity. However, there are significant challenges to overcome, such as the lack of sufficient training data and the difficulty of modeling complex and dynamic attack scenarios hindering researchers’ ability to address real-world challenges and advance the state of the art in RL cyber applications. In this work, we applied a deep RL (DRL) framework in adversarial cyber-attack simulation to enhance cybersecurity. Our framework uses an agent-based model to continuously learn from and adapt to the dynamic and uncertain environment of network security. The agent decides on the optimal attack actions to take based on the state of the network and the rewards it receives for its decisions. Our experiments on synthetic network security show that the DRL approach outperforms existing methods in terms of learning optimal attack actions. Our framework represents a promising step towards the development of more effective and dynamic cybersecurity solutions.
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spelling pubmed-100513292023-03-30 Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation Oh, Sang Ho Jeong, Min Ki Kim, Hyung Chan Park, Jongyoul Sensors (Basel) Article Cybersecurity is a growing concern in today’s interconnected world. Traditional cybersecurity approaches, such as signature-based detection and rule-based firewalls, are often limited in their ability to effectively respond to evolving and sophisticated cyber threats. Reinforcement learning (RL) has shown great potential in solving complex decision-making problems in various domains, including cybersecurity. However, there are significant challenges to overcome, such as the lack of sufficient training data and the difficulty of modeling complex and dynamic attack scenarios hindering researchers’ ability to address real-world challenges and advance the state of the art in RL cyber applications. In this work, we applied a deep RL (DRL) framework in adversarial cyber-attack simulation to enhance cybersecurity. Our framework uses an agent-based model to continuously learn from and adapt to the dynamic and uncertain environment of network security. The agent decides on the optimal attack actions to take based on the state of the network and the rewards it receives for its decisions. Our experiments on synthetic network security show that the DRL approach outperforms existing methods in terms of learning optimal attack actions. Our framework represents a promising step towards the development of more effective and dynamic cybersecurity solutions. MDPI 2023-03-10 /pmc/articles/PMC10051329/ /pubmed/36991711 http://dx.doi.org/10.3390/s23063000 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Oh, Sang Ho
Jeong, Min Ki
Kim, Hyung Chan
Park, Jongyoul
Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation
title Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation
title_full Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation
title_fullStr Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation
title_full_unstemmed Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation
title_short Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation
title_sort applying reinforcement learning for enhanced cybersecurity against adversarial simulation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10051329/
https://www.ncbi.nlm.nih.gov/pubmed/36991711
http://dx.doi.org/10.3390/s23063000
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