Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents
With sophisticated cyber-attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber-defense agents trained via Reinforcement Learning (RL). These agents employ neurosymbolic approaches such as behavior trees with learning-enabled components (LECs) to learn, reason, adapt, and implement security rules while maintaining critical operations. However, these autonomous networks are partially observable systems, i.e., the cyber-attacker's (red agent's) actions are not observable, making it difficult for the defender to predict red actions, learn red policies, or assess the attacker's intrusion levels. To address this, we propose a Policy Learning Technique usi...
| Authors | Ankita Samaddar, Sandeep Neema, Daniel Balasubramanian et al. |
|---|---|
| Primary category | cs.CR |
| Published | 16 June 2026 |
| Last updated at source | 14 July 2026 |
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