Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks

TL;DR

This paper reviews reinforcement learning-based methods for efficient course-of-action attack search in large-scale network security, highlighting recent trends and advancements in the field.

Contribution

It provides a comprehensive overview of RL-based COA attack search methods, emphasizing their development, challenges, and potential in modern network security.

Findings

RL-based methods improve search efficiency in large networks

Recent trends show increased adoption of intelligent algorithms

Challenges include resource constraints and scalability

Abstract

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