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CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents
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CybORG++ is an advanced toolkit for reinforcement learning research focused on network defence. Building on the CAGE 2 CybORG environment, it introduces key improvements, including enhanced debugging capabilities, refined agent implementation support, and a streamlined environment that enables faster training and easier customisation. Along with addressing several software bugs from its predecessor, CybORG++ introduces MiniCAGE, a lightweight version of CAGE 2, which improves performance dramatically, up to 1000x faster execution in parallel iterations, without sacrificing accuracy or core functionality. CybORG++ serves as a robust platform for developing and evaluating defensive agents, making it a valuable resource for advancing enterprise network defence research.
Forward citations
Cited by 3 Pith papers
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Beyond Rewards in Reinforcement Learning for Cyber Defence
Sparse, goal-aligned rewards outperform dense engineered rewards for training cyber-defence RL agents, but the advantage depends on the evaluation metric and is not uniformly confirmed in the more complex CAGE environment.
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Reinforcement Learning for Automated Cybersecurity Penetration Testing
An RL agent using permutation-symmetric neural networks automates web pentesting on simulated sites and is claimed to find all reachable vulnerabilities on DVWA and DockerLabs.
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Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
A narrative survey of multi-agent reinforcement learning for cyber defense, reviewing game-theoretic models, cyber gyms, and applications, concluding MARL is promising but faces scalability and simulation-to-real tran...
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