# Deep Reinforcement Learning for Cyber Security

arXiv: https://arxiv.org/abs/1906.05799 · HTML: https://mlsanity.com/paper/1906.05799/

## Key facts

- Cited by 95 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 30 of 95)

- [Reinforcement Learning for IoT Security: A Comprehensive Survey](https://mlsanity.com/paper/2102.07247/) (2021)
- [The Role of Machine Learning in Cybersecurity](https://mlsanity.com/paper/2206.09707/) (2022)
- [Automated Cyber Defence: A Review](https://mlsanity.com/paper/2303.04926/) (2023)
- [Reinforcement Learning for Feedback-Enabled Cyber Resilience](https://mlsanity.com/paper/2107.00783/) (2021)
- [Intrusion Prevention through Optimal Stopping](https://mlsanity.com/paper/2111.00289/) (2021)
- [Inroads into Autonomous Network Defence using Explained Reinforcement Learning](https://mlsanity.com/paper/2306.09318/) (2023)
- [Learning Near-Optimal Intrusion Responses Against Dynamic Attackers](https://mlsanity.com/paper/2301.06085/) (2023)
- [Autonomous Network Defence using Reinforcement Learning](https://mlsanity.com/paper/2409.18197/) (2024)
- [Automated Security Response through Online Learning with Adaptive Conjectures](https://mlsanity.com/paper/2402.12499/) (2024)
- [Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.05939/) (2023)
- [Real-time Network Intrusion Detection via Decision Transformers](https://mlsanity.com/paper/2312.07696/) (2023)
- [Prospective Artificial Intelligence Approaches for Active Cyber Defence](https://mlsanity.com/paper/2104.09981/) (2021)
- [Learning Intrusion Prevention Policies through Optimal Stopping](https://mlsanity.com/paper/2106.07160/) (2021)
- [CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems](https://mlsanity.com/paper/2109.03331/) (2021)
- [ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning](https://mlsanity.com/paper/2208.12897/) (2022)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [Entity-based Reinforcement Learning for Autonomous Cyber Defence](https://mlsanity.com/paper/2410.17647/) (2024)
- [Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning](https://mlsanity.com/paper/2312.04940/) (2023)
- [Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection](https://mlsanity.com/paper/2111.13978/) (2021)
- [Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs](https://mlsanity.com/paper/2201.12416/) (2022)
- [A Multiagent CyberBattleSim for RL Cyber Operation Agents](https://mlsanity.com/paper/2304.11052/) (2023)
- [Impacts and Risk of Generative AI Technology on Cyber Defense](https://mlsanity.com/paper/2306.13033/) (2023)
- [Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey](https://mlsanity.com/paper/2411.05614/) (2024)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Execute Order 66: Targeted Data Poisoning for Reinforcement Learning](https://mlsanity.com/paper/2201.00762/) (2022)
- [RLocator: Reinforcement Learning for Bug Localization](https://mlsanity.com/paper/2305.05586/) (2023)
- [Structural Generalization in Autonomous Cyber Incident Response with Message-Passing Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2407.05775/) (2024)
- [DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift](https://mlsanity.com/paper/2508.18839/) (2025)
- [Network Defense is Not a Game](https://mlsanity.com/paper/2104.10262/) (2021)
- [Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee](https://mlsanity.com/paper/2405.15245/) (2024)

## Abstract (arXiv)

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.