Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents

2026-06-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A new Policy Learning Technique is proposed to enhance neurosymbolic autonomous cyber-defense agents by addressing the challenge of partially observable systems. Modern networks rely on these agents, which use neurosymbolic approaches like behavior trees with learning-enabled components (LECs), to learn, reason, and adapt security rules. However, the unobservable actions of cyber-attackers (red agents) hinder defenders' ability to predict red actions or assess intrusion levels. This technique employs imitation learning to predict red agent actions from network observations and defender actions, specifically for partially observable Reinforcement Learning (RL) agents with discrete states and actions. When integrated into a neurosymbolic cyber-defense agent within an autonomous cyber environment, the method effectively manages various red policies and demonstrates high prediction accuracy across diverse simulated scenarios.

Key takeaway

For AI Security Engineers developing autonomous cyber defense systems, understanding and predicting red agent behavior is critical. You should integrate imitation learning to infer attacker policies from network observations and your defender actions, especially in partially observable environments. This approach improves your agent's ability to adapt security rules and assess intrusion levels, enhancing network resilience against sophisticated cyber-attacks.

Key insights

Imitation learning predicts unobservable cyber-attacker actions, enhancing neurosymbolic defense in partially observable networks.

Principles

• Cyber defense needs red agent action prediction.
• Neurosymbolic agents adapt security rules.
• Policy learning aids partially observable RL.

Method

A Policy Learning Technique uses imitation learning to predict red agent actions from network observations and defender actions for partially observable RL agents with discrete states and actions.

In practice

• Integrate imitation learning into neurosymbolic cyber-defense.
• Apply policy learning in autonomous cyber environments.
• Infer attacker behavior from network observations.

Topics

• Neurosymbolic AI
• Cyber Defense
• Imitation Learning
• Reinforcement Learning
• Partially Observable Systems
• Red Agent Policy

Best for: Research Scientist, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.