基于大型语言模型代理的网络攻击和防御的自动化策略规划
Yimo Ren1, Jinfa Wang1, Zhihui Zhao2
1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China.
概括
大型语言模型 (LLM) 与强化学习 (RL) 结合,可实现自动化网络攻击和防御策略规划. 这种方法显著提高了网络安全策略的有效性和适应性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 网络事件的复杂性和频率正在增加.
- 传统的网络安全策略规划不足以提供及时有效的响应.
- 先进的自动化解决方案对攻击者和防御者都至关重要.
研究的目的:
- 从强化学习 (RL) 角度探索大型语言模型 (LLM),用于自动化网络安全策略规划.
- 为网络攻击和防御场景开发更复杂和更适应的战术.
- 为了利用LLM的自然语言处理能力进行战略规划.
主要方法:
- 构建了一个公开可用的模拟环境,用于网络攻击和防御.
- 建议在RL框架内使用LLM的战术代理.
- 在模拟环境中进行了广泛的实验 (近一千次).
主要成果:
- 提出的基于LLM的战术代理显示了自动化战术规划的显著改进.
- 实验结果验证了在网络攻击和防御场景中提高有效性和适应性.
- 该方法为推进自动化网络安全策略提供了一个有希望的方向.
结论:
- 当LLM与RL集成时,它为自动化网络安全策略提供了一个强大的框架.
- 开发的代理显示了创造更复杂和响应性更强的网络防御和攻击策略的潜力.
- 这项研究为未来人工智能驱动的网络安全创新铺平了道路.
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