Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks
It provides a summary of existing methods for network security researchers and practitioners, but is incremental as it only reviews prior work.
This tutorial reviews reinforcement learning-based methods for course-of-action attack search in computer networks, addressing scalability issues as network size increases, but does not present new results or concrete numbers.
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