用大模型设计奖励机制，让自动防御系统学会应对多样攻击
Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense
- 用大模型根据环境信息生成防御奖励规则
- 在仿真环境中学习出多类有效防御策略
- 适合安全研究者和自动化攻防系统开发者
在复杂动态环境中，为自主网络攻防学习代理设计奖励机制对领域专家而言极具挑战。本文提出一种基于大语言模型（LLM）的奖励设计方法，在深度强化学习（DRL）驱动的实验仿真环境中生成自主防御策略。构建了反映代理行为异质性的多种攻防代理角色，将上下文环境信息提供给LLM以生成引导性奖励设计。这些奖励结构被用于DRL攻防仿真环境，以学习一组集成的网络安全防御策略。结果表明，由LLM引导的奖励设计能有效应对多样化对抗行为。
原文摘要 · Abstract (English)
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.
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