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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/6/3000 |
| _version_ | 1827747757900693504 |
|---|---|
| author | Sang Ho Oh Min Ki Jeong Hyung Chan Kim Jongyoul Park |
| author_facet | Sang Ho Oh Min Ki Jeong Hyung Chan Kim Jongyoul Park |
| author_sort | Sang Ho Oh |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T05:56:47Z |
| format | Article |
| id | doaj.art-f18644bf9d2944b1a30a8b1fba2b2b00 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:56:47Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f18644bf9d2944b1a30a8b1fba2b2b002023-11-17T13:44:31ZengMDPI AGSensors1424-82202023-03-01236300010.3390/s23063000Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial SimulationSang Ho Oh0Min Ki Jeong1Hyung Chan Kim2Jongyoul Park3Business Department of Convergence and Open Sharing System, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaDepartment of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaThe Affiliated Institute of Electronics and Telecommunications Research Institute, Daejeon 34044, Republic of KoreaBusiness Department of Convergence and Open Sharing System, Seoul National University of Science and Technology, Seoul 01811, Republic of KoreaCybersecurity 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.https://www.mdpi.com/1424-8220/23/6/3000deep reinforcement learningcybersecurityadversarial simulationartificial intelligence |
| spellingShingle | Sang Ho Oh Min Ki Jeong Hyung Chan Kim Jongyoul Park Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation Sensors deep reinforcement learning cybersecurity adversarial simulation artificial intelligence |
| 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 | deep reinforcement learning cybersecurity adversarial simulation artificial intelligence |
| url | https://www.mdpi.com/1424-8220/23/6/3000 |
| work_keys_str_mv | AT sanghooh applyingreinforcementlearningforenhancedcybersecurityagainstadversarialsimulation AT minkijeong applyingreinforcementlearningforenhancedcybersecurityagainstadversarialsimulation AT hyungchankim applyingreinforcementlearningforenhancedcybersecurityagainstadversarialsimulation AT jongyoulpark applyingreinforcementlearningforenhancedcybersecurityagainstadversarialsimulation |