Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution
An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability to generalize as network scale changes. Hierarchical RL reduces decision complexity by separating strategic targeting from tactical execution, but it does not eliminate this retraining dependence. We investigate whether frozen, zero-shot large language models (LLMs) can provide retraining-free control in hierarchical cyber defense and how performance changes as LLM control is extended from planning to execution. We formulate a controller-agnostic planner-executor hierarchy in which the planner selects a subnet to defend over a fixed horizon a...
| Authors | Harshith Doppalapudi, Nathaniel D. Bastian, Ankit Shah |
|---|---|
| Primary category | cs.CR |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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