TLDR: This research introduces a framework to make simulated environments for Autonomous Cyber Operations (ACO) more realistic and effective for training Reinforcement Learning (RL) agents. The study extends CybORG’s Cage Challenge 2 environment by adding new actions like Patch, Isolate, and Unisolate, which better reflect real-world human operator capabilities. It also optimizes RL training by modifying reward signals and the agent’s feature space. Through empirical evaluation, the paper demonstrates that Proximal Policy Optimization (PPO) agents outperform Deep Q-Network (DQN) agents in this enhanced environment, confirming that CybORG can be augmented with realistic functionalities while still providing strong training signals for AI.
In the rapidly evolving landscape of cybersecurity, the demand for automated defense mechanisms is growing. Autonomous Cyber Operations (ACO), powered by Reinforcement Learning (RL), offer a promising solution by training AI agents to make effective decisions with minimal human intervention. However, a significant challenge lies in creating simulated environments that accurately mirror the complexities of real-world cyber scenarios, allowing these agents to be trained without the high computational costs of physical emulation.
A recent research paper, “Towards Production-Worthy Simulation for Autonomous Cyber Operations,” by Konur Tholl, Mariam El-Mezouar, and Ranwa Al Mallah, addresses this critical gap. The study presents a novel framework designed to enhance the realism and training efficiency of simulated environments for ACO, specifically by extending the popular CybORG’s Cage Challenge 2 environment. You can read the full paper here: Towards Production-Worthy Simulation for Autonomous Cyber Operations.
Expanding the Agent’s Toolkit: New Actions for Realistic Defense
One of the core contributions of this research is the augmentation of CybORG’s action space. Traditionally, simulated environments might limit the actions available to a defending (blue) agent, which doesn’t fully reflect the diverse capabilities of human operators. To bridge this gap, the researchers implemented three crucial new actions:
- Patch: This action simulates updating vulnerable services on a host, significantly decreasing the likelihood of an attacker successfully exploiting it. The system tracks a “patch score” for each host, which the Patch action increases, while attacker actions decrease it, mimicking the dynamic nature of vulnerabilities.
- Isolate: Simulating the physical disconnection of a host from the network, this action prevents any incoming or outgoing connectivity. While effective in stopping attacks, isolating a host incurs a negative reward, reflecting the operational disruption it causes. Importantly, isolation doesn’t remove existing attacker sessions, emphasizing the need for further defensive actions once reconnected.
- Unisolate: This action allows an isolated host to be rejoined to the network, restoring its connectivity.
To streamline the environment and focus on proactive defense, the study also removed certain deception-based actions (like honeypots) and the redundant Monitor action from CybORG’s original setup. The refined action space now includes Analyze, Restore, Remove, Isolate, Unisolate, and Patch.
Optimizing Training Signals and State Representation
Beyond expanding the action space, the research also focused on refining the signals that guide RL agents during training. The agent’s “state space” – the information it perceives about the network – was enhanced to include the isolated and patch status of hosts. Existing information, such as activity and compromised states, was converted from complex one-hot encodings to normalized floating-point values. This not only reduces the feature space but also helps the agent prioritize more impactful states during learning. Additionally, the total number of isolated and compromised hosts, normalized by the total number of hosts, was added to the state space.
A significant modification was made to CybORG’s reward signal. Originally, agents received a negative reward at every timestep, which could make it difficult to distinguish between good and bad actions. The researchers normalized the reward signal to fall within a range of [-2.5, 2.5]. This clearer distinction between desirable and undesirable actions significantly improves the agent’s learning efficiency and performance, laying the groundwork for more advanced, teacher-guided training techniques in the future.
Evaluating Agent Performance: PPO Outperforms DQN
To validate these modifications, the researchers developed and trained two types of baseline RL agents: Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). Through extensive hyperparameter tuning and evaluation, the study found that PPO agents consistently outperformed DQN agents. PPO demonstrated smoother convergence during training, indicating its superior ability to learn optimal defense policies in the modified environment.
Further optimization of the PPO algorithm, using advanced techniques like Bayesian optimization (Optuna), entropy bonuses, and gradient clipping, led to even more stable and efficient learning. The evaluation confirmed that the enhanced CybORG environment, with its more realistic actions and optimized signals, still enables agents to learn effective defense strategies, demonstrating the potential for creating more operationally relevant simulations.
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Looking Ahead: Towards Production-Ready Cyber Agents
While this study marks a significant step towards more realistic ACO environments, the authors acknowledge several limitations and outline exciting avenues for future work. These include increasing the complexity of simulated hosts (more processes and files), breaking the unrealistic sequential execution of actions by red and blue agents, and improving the mapping of CybORG’s raw output to RL agent features, potentially using neural networks to capture non-linear patterns.
This research underscores the ongoing effort to bridge the gap between simulated training environments and the demands of real-world autonomous cyber operations. By making simulations more realistic and training more efficient, the work contributes to the development of production-quality AI agents capable of defending our digital infrastructure.
