论文标题
时空攻击行动课程（COA）搜索学习的可扩展性和随时间变化的网络
Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks
论文作者
论文摘要
网络安全研究的关键主题之一是自主COA（行动式）攻击搜索方法。被动搜索攻击的传统COA攻击方法可能很困难，尤其是在网络变得更大时。为了解决这些问题，正在开发新的自主COA技术，其中，本文设计了一种智能的空间算法，以在可扩展网络中有效运行。除空间搜索外，还考虑了基于蒙特卡洛（MC）的时间方法来照顾时间变化的网络行为。因此，我们为可扩展和时变网络的时空攻击COA搜索算法提出了时空攻击。
One of the key topics in network security research is the autonomous COA (Couse-of-Action) attack search method. Traditional COA attack search methods that passively search for attacks can be difficult, especially as the network gets bigger. To address these issues, new autonomous COA techniques are being developed, and among them, an intelligent spatial algorithm is designed in this paper for efficient operations in scalable networks. On top of the spatial search, a Monte-Carlo (MC)- based temporal approach is additionally considered for taking care of time-varying network behaviors. Therefore, we propose a spatio-temporal attack COA search algorithm for scalable and time-varying networks.
