为安全领域应用强化学习提供实用指南
Guidelines for Applying RL and MARL in Cybersecurity Applications
- 梳理RL/MARL在网络安全中的适用场景与评估标准
- 指出可解释性、探索需求与多智能体协作等核心挑战
- 适合从事智能防御系统研发的工程师与研究者参考
强化学习（RL）和多智能体强化学习（MARL）已成为应对自动化网络安全防御（ACD）挑战的有前景方法。这些技术在高维对抗性环境中具备自适应决策能力。本报告为网络安全从业者与研究人员提供一套结构化指南，用于评估特定应用场景下使用RL和MARL的可行性，考虑因素包括可解释性、探索需求以及多智能体协调的复杂性。报告还讨论了关键算法方法、实施挑战及现实约束，如数据稀缺性和对抗干扰。此外，列出了若干开放研究问题，包括策略最优性、智能体合作程度以及将MARL系统集成到实际网络安全框架中的方法。通过弥合理论进展与实际部署之间的差距，这些指南旨在提升基于AI的网络安全防御策略的有效性。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL) have emerged as promising methodologies for addressing challenges in automated cyber defence (ACD). These techniques offer adaptive decision-making capabilities in high-dimensional, adversarial environments. This report provides a structured set of guidelines for cybersecurity professionals and researchers to assess the suitability of RL and MARL for specific use cases, considering factors such as explainability, exploration needs, and the complexity of multi-agent coordination. It also discusses key algorithmic approaches, implementation challenges, and real-world constraints, such as data scarcity and adversarial interference. The report further outlines open research questions, including policy optimality, agent cooperation levels, and the integration of MARL systems into operational cybersecurity frameworks. By bridging theoretical advancements and practical deployment, these guidelines aim to enhance the effectiveness of AI-driven cyber defence strategies.
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