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[2508.19278] Towards Production-Worthy Simulation for Autonomous Cyber Operations
Summary
This article presents a framework for enhancing simulation environments in Autonomous Cyber Operations (ACO) by implementing new actions and modifying training signals for reinforcement learning agents.
Why It Matters
As cybersecurity threats evolve, effective training of autonomous systems is crucial. This study advances the capabilities of simulation environments, making them more realistic and effective for training AI agents, which can lead to improved cybersecurity strategies and responses.
Key Takeaways
- The study enhances CybORG's Cage Challenge 2 environment with new actions for better realism.
- Modifications to reward signals and feature space improve reinforcement learning agent training.
- Validation through training DQN and PPO agents demonstrates the effectiveness of the proposed framework.
Computer Science > Cryptography and Security arXiv:2508.19278 (cs) [Submitted on 23 Aug 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Towards Production-Worthy Simulation for Autonomous Cyber Operations Authors:Konur Tholl, Mariam El Mezouar, Adrian Taylor, Ranwa Al Mallah View a PDF of the paper titled Towards Production-Worthy Simulation for Autonomous Cyber Operations, by Konur Tholl and 2 other authors View PDF HTML (experimental) Abstract:Simulated environments have proven invaluable in Autonomous Cyber Operations (ACO) where Reinforcement Learning (RL) agents can be trained without the computational overhead of emulation. These environments must accurately represent cybersecurity scenarios while producing the necessary signals to support RL training. In this study, we present a framework where we first extend CybORG's Cage Challenge 2 environment by implementing three new actions: Patch, Isolate, and Unisolate, to better represent the capabilities available to human operators in real-world settings. We then propose a design for agent development where we modify the reward signals and the agent's feature space to enhance training performance. To validate these modifications, we train DQN and PPO agents in the updated environment. Our study demonstrates that CybORG can be extended with additional realistic functionality, while maintaining its ability to generate informative training signals for RL agents. Subjects: Cryptography and Security (cs.CR); Art...
