Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914165708292096 |
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| author | Mukherjee, Sayak Chatterjee, Samrat Purvine, Emilie Fujimoto, Ted Emerson, Tegan |
| author_facet | Mukherjee, Sayak Chatterjee, Samrat Purvine, Emilie Fujimoto, Ted Emerson, Tegan |
| contents | Designing rewards for autonomous cyber attack and defense learning agents in a complex, dynamic environment is a challenging task for subject matter experts. We propose a large language model (LLM)-based reward design approach to generate autonomous cyber defense policies in a deep reinforcement learning (DRL)-driven experimental simulation environment. Multiple attack and defense agent personas were crafted, reflecting heterogeneity in agent actions, to generate LLM-guided reward designs where the LLM was first provided with contextual cyber simulation environment information. These reward structures were then utilized within a DRL-driven attack-defense simulation environment to learn an ensemble of cyber defense policies. Our results suggest that LLM-guided reward designs can lead to effective defense strategies against diverse adversarial behaviors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16483 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense Mukherjee, Sayak Chatterjee, Samrat Purvine, Emilie Fujimoto, Ted Emerson, Tegan Machine Learning Artificial Intelligence Multiagent Systems Designing rewards for autonomous cyber attack and defense learning agents in a complex, dynamic environment is a challenging task for subject matter experts. We propose a large language model (LLM)-based reward design approach to generate autonomous cyber defense policies in a deep reinforcement learning (DRL)-driven experimental simulation environment. Multiple attack and defense agent personas were crafted, reflecting heterogeneity in agent actions, to generate LLM-guided reward designs where the LLM was first provided with contextual cyber simulation environment information. These reward structures were then utilized within a DRL-driven attack-defense simulation environment to learn an ensemble of cyber defense policies. Our results suggest that LLM-guided reward designs can lead to effective defense strategies against diverse adversarial behaviors. |
| title | Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense |
| topic | Machine Learning Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2511.16483 |