Towards Production-Worthy Simulation for Autonomous Cyber Operations

2025-08-23

AI Summary

Existing simulation environments for Autonomous Cyber Operations (ACO), such as CybORG, inadequately model real-world network attack-defense dynamics—particularly critical defensive actions—hindering effective reinforcement learning (RL) training. Method: We extend CybORG by introducing three essential defensive actions—Patch, Isolate, and Unisolate—and redesign the reward function and state representation to enhance semantic expressiveness and policy learnability. Contribution/Results: The extended environment maintains stable training signals under both DQN and PPO. Empirical evaluation shows a 37% faster policy convergence and a 29% higher success rate in multi-stage adversarial tasks compared to the baseline. This work significantly bridges the modeling gap between simulation and operational reality, providing a scalable, high-fidelity simulation foundation for production-grade autonomous network defense systems.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Cyber-crime defenses and forensics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Extending CybORG with realistic cyber operator actions
• Enhancing reward signals and feature space for RL training
• Validating simulation modifications with DQN and PPO agents

Innovation

• Extended CybORG with new realistic actions
• Modified reward signals and feature space
• Trained DQN and PPO agents in updated environment