Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents

Problem — The paper addresses the challenge of training autonomous cyber-defense agents in partially observable environments, where the actions of adversarial agents (red agents) are not directly observable. This gap in capability is critical as it hampers the defender’s ability to predict adversarial strategies, learn effective countermeasures, and assess intrusion levels. The authors highlight that existing literature lacks robust methodologies for policy learning in such scenarios, particularly using imitation learning techniques. This work is presented as a preprint, indicating it has not yet undergone peer review.

Method — The authors propose a Policy Learning Technique that leverages imitation learning to derive policies for partially observable reinforcement learning (RL) agents. The architecture integrates behavior trees with learning-enabled components (LECs) to facilitate reasoning and adaptation in cyber-defense tasks. The method involves training the agent on network observations and defender actions to infer the red agent’s policies. The training process utilizes a discrete state and action space, optimizing the learning process for the specific characteristics of the cyber environment. The computational resources and specific training parameters are not disclosed, but the approach emphasizes the importance of simulating diverse scenarios to enhance the robustness of the learned policies.

Results — The proposed technique demonstrates high prediction accuracy in various simulated environments, outperforming baseline methods in terms of action prediction for the red agent. While specific numerical results are not provided in the abstract, the authors claim significant improvements over traditional methods, indicating that their approach effectively adapts to different red policies. The results suggest that the imitation learning framework can generalize well across diverse attack scenarios, although exact metrics and comparisons to named baselines are not detailed in the summary.

Limitations — The authors acknowledge that their approach may be limited by the quality and diversity of the training data, as well as the inherent challenges of modeling complex adversarial behaviors. They do not address potential scalability issues when applied to larger, more complex networks or the computational overhead associated with real-time policy updates. Additionally, the reliance on simulated environments may not fully capture the unpredictability of real-world cyber-attacks, which could affect the generalizability of the learned policies.

Why it matters — This work has significant implications for the development of intelligent autonomous cyber-defense systems capable of adapting to evolving threats in real-time. By enhancing the ability to predict adversarial actions, the proposed method could lead to more effective defense strategies and improved network resilience. The integration of neurosymbolic approaches with imitation learning represents a promising direction for future research in autonomous cyber-defense, as highlighted in related works on reinforcement learning and adversarial machine learning, as published in arXiv cs.AI.