论文标题
基于强化学习的网络空间配置的弱点分析
Weakness Analysis of Cyberspace Configuration Based on Reinforcement Learning
论文作者
论文摘要
在这项工作中，我们提出了一种基于学习的方法来分析网络空间配置。与先前的方法不同，我们的方法具有从过去的经验中学习并随着时间的推移而改善的能力。特别是，当我们训练更多的代理商作为攻击者时，我们的方法在快速寻找以前隐藏的路径的攻击路径方面变得更好，尤其是在多个域网络空间中。为了实现这些结果，我们构成寻找攻击路径作为加强学习（RL）问题，并培训代理以找到多个域攻击路径。为了使我们的RL策略找到更多隐藏的攻击路径，我们基础表示RL中的多个域操作选择模块。通过设计模拟的网络空间实验环境来验证我们的方法。我们的目标是找到更多隐藏的攻击路径，以分析网络空间配置的弱点。实验结果表明，与现有基线方法相比，我们的方法可以找到更多的隐藏多个域攻击路径。
In this work, we present a learning-based approach to analysis cyberspace configuration. Unlike prior methods, our approach has the ability to learn from past experience and improve over time. In particular, as we train over a greater number of agents as attackers, our method becomes better at rapidly finding attack paths for previously hidden paths, especially in multiple domain cyberspace. To achieve these results, we pose finding attack paths as a Reinforcement Learning (RL) problem and train an agent to find multiple domain attack paths. To enable our RL policy to find more hidden attack paths, we ground representation introduction an multiple domain action select module in RL. By designing a simulated cyberspace experimental environment to verify our method. Our objective is to find more hidden attack paths, to analysis the weakness of cyberspace configuration. The experimental results show that our method can find more hidden multiple domain attack paths than existing baselines methods.
