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Detail
Journal Article
강화학습 기반 사이버 공격 시뮬레이션 및 에뮬레이션 환경 기술 동향
- Issue Date
- 2026-02
- Citation
- 전자통신동향분석, v.41, no.1, pp.82-95
- ISSN
- 1225-6455
- Publisher
- 한국전자통신연구원
- Language
- Korean
- Type
- Journal Article
- Abstract
- With the increasing sophistication and automation of cyberattacks, simulation and emulation environments that replicate attack processes in virtual settings have become essential for evaluating defensive strategies. Moreover, research has been conducted on the application of (RL) in cyber ranges to enable autonomous penetration testing. This study provides a comparative analysis of the characteristics and limitations of RL-based cyber-attack simulation environments, including Network Attack Simulation, Cyber Battle Simulation,and the Autonomous Pentesting framework based on Reinforcement Learning, as well as emulation environments such as Cyber Game for Intelligent Learning and Pentesting Gym. In addition, this paper summarizes the characteristics of hybrid environments, including Network Attack Simulation and Emulation and the Generalizable Autonomous Pentesting Framework. In particular, the analysis includes structural differences, Markov decision process design, scalability to large-scale networks, and generalization capability across diverse scenarios. In the future, providing high-fidelity learning environments that closely resemble real networks will require integrated training architectures that combine simulation and emulation, along with expansion toward multi-agent environments.
- Keyword
- Cyber Attack, Emulation, Generalization, Penetration Testing, Reinforcement Learning, Scalability, Simulation
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
