In the literature of modern network security research, deriving effective and efficient course-of-action (COA) attach search methods are of interests in industry and academia. As the network size grows, the traditional COA attack search methods can suffer from the limitations to computing and communication resources. Therefore, various methods have been developed to solve these problems, and reinforcement learning (RL)-based intelligent algorithms are one of the most effective solutions. Therefore, we review the RL-based COA attack search methods for network attack scenarios in terms of the trends and their contrib
翻译:在现代网络安全研究的文献中,从有效、高效的行动路线中引出有效、高效的搜索方法,是产业和学术界所感兴趣的。随着网络规模的扩大,传统的COA攻击搜索方法可能受到计算和通信资源的局限。因此,已经开发了各种方法来解决这些问题,强化学习(RL)为基础的智能算法是最有效的解决办法之一。因此,我们从趋势及其配置的角度审查以RL为基础的网络攻击情景的COA攻击搜索方法。