论文标题
通过增强学习，攻击图和网络地形揭示监视检测路线
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain
论文作者
论文摘要
在攻击图上运行的强化学习（RL）利用网络地形原则用于开发与确定监视检测途径（SDR）相关的奖励和状态。这项工作扩展了以前在开发企业网络中用于路径分析的RL方法的努力。这项工作着重于构建SDR，该路线专注于探索网络服务，同时试图逃避风险。 RL用于通过建立有助于实现这些路径的奖励机制来支持这些路线的发展。 RL算法被修改为具有新型的热身阶段，该阶段在初始探索中决定了网络的哪个区域可以根据奖励和惩罚量表因子安全探索。
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR). This work extends previous efforts on developing RL methods for path analysis within enterprise networks. This work focuses on building SDR where the routes focus on exploring the network services while trying to evade risk. RL is utilized to support the development of these routes by building a reward mechanism that would help in realization of these paths. The RL algorithm is modified to have a novel warm-up phase which decides in the initial exploration which areas of the network are safe to explore based on the rewards and penalty scale factor.
