# Deep Reinforcement Learning for Cyber Security

## TL;DR

This paper surveys the application of deep reinforcement learning techniques to enhance cyber security, addressing complex, dynamic, and high-dimensional defense challenges through various DRL-based methods and strategies.

## Contribution

It provides a comprehensive review of DRL approaches in cyber security, highlighting recent developments, applications, and future research directions.

## Key findings

- DRL effectively addresses complex cyber defense problems.
- Multiple DRL-based intrusion detection and defense strategies are discussed.
- Future research directions include scalability and real-world deployment challenges.

## Abstract

The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This paper presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multiagent DRL-based game theory simulations for defense strategies against cyber attacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems.