AI 中文总结
该研究通过对比实验证明，大型语言模型（LLMs）驱动的网络安全模拟方法，相比经典强化学习方法，在网络攻击模拟中效率更高、适应性更强，且速度提升达25000至50000倍，为构建智能网络防御系统提供了新路径。
AI 中文摘要
网络威胁的频率和复杂性持续升级，需要更具适应性和可扩展性的防御策略。本文探讨大型语言模型（LLMs）如何通过自动创建合成环境和识别潜在漏洞来强化网络安全模拟。我们采用YAML作为结构化表示格式来模拟复杂网络配置，从而实现由大型语言模型驱动的流程支持并改进强化学习（RL）智能体训练。对比研究考察了基于LLM的技术与经典方法（如带优先经验回放（PER）的Double Q-learning）相比的优势，强调其在网络攻击模拟中效率更高、适应性更强、真实性更优。在多个合成拓扑的实证基准测试中，由LLM实例化的Python智能体实现了高达94.5%的入侵成功率，且每次评估耗时0.02-0.06秒，相比传统RL训练周期实现了约25000倍至50000倍的加速。我们的研究结果强调了将LLMs融入网络安全研究的变革潜力，最终为构建更智能、更强大的网络防御系统铺平道路。
英文摘要
Cyber threats continue to escalate in both frequency and sophistication, necessitating more adaptive and scalable defense strategies. This paper explores how Large Language Models (LLMs) can bolster cybersecurity simulations by automating the creation of synthetic environments and identifying latent vulnerabilities. We employ YAML as a structured representation format for simulating complex network configurations, thereby enabling Large Language Model-driven pipelines to support and improve reinforcement learning (RL) agent training. Comparative studies examine the advantages of LLM-based techniques over classical approaches such as Double Q-learning with Prioritized Experience Replay (PER), emphasizing increased efficiency, higher adaptability, and enhanced realism in cyberattack simulations. In empirical benchmarks across multiple synthetic topologies, LLM-instantiated Python agents achieved up to a 94.5% compromise rate while executing in 0.02-0.06 seconds per assessment---a ~25,000x to 50,000x speedup over traditional RL training cycles. Our findings underscore the transformative potential of integrating LLMs into cybersecurity research, ultimately paving the way for more intelligent and robust cyber-defense systems.
Comments13 pages, 4 figures, 2 tables
