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Deep Reinforcement Learning for Cyber Security

arXiv 1906.05799 ↗First posted 13 Jun 2019cs.CR cs.AI cs.LGcited by 92

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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.

Key facts. Cited by 92 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 12; 2023: 17; 2024: 18; 2025: 13; 2026: 15.

92 papers.

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  1. 2021Reinforcement Learning for IoT Security: A Comprehensive Surveycited by 21
  2. 2022The Role of Machine Learning in Cybersecuritycited by 21
  3. 2023Automated Cyber Defence: A Reviewcited by 19
  4. 2021Reinforcement Learning for Feedback-Enabled Cyber Resiliencecited by 18
  5. 2021Intrusion Prevention through Optimal Stoppingcited by 18
  6. 2023Inroads into Autonomous Network Defence using Explained Reinforcement Learningcited by 17
  7. 2024Autonomous Network Defence using Reinforcement Learningcited by 14
  8. 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackerscited by 13
  9. 2024Automated Security Response through Online Learning with Adaptive Conjecturescited by 13
  10. 2023Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learningcited by 12
  11. 2023Real-time Network Intrusion Detection via Decision Transformerscited by 12
  12. 2021Prospective Artificial Intelligence Approaches for Active Cyber Defencecited by 11
38 more
  1. 2021Learning Intrusion Prevention Policies through Optimal Stoppingcited by 11
  2. 2021CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systemscited by 11
  3. 2022ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learningcited by 11
  4. 2024Entity-based Reinforcement Learning for Autonomous Cyber Defencecited by 11
  5. 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learningcited by 9
  6. 2023Vision-based Learning for Drones: A Surveycited by 9
  7. 2021Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detectioncited by 8
  8. 2022Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphscited by 8
  9. 2023A Multiagent CyberBattleSim for RL Cyber Operation Agentscited by 8
  10. 2023Impacts and Risk of Generative AI Technology on Cyber Defensecited by 8
  11. 2024Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Surveycited by 8
  12. 2021Machine Learning for Security in Vehicular Networks: A Comprehensive Surveycited by 7
  13. 2022Execute Order 66: Targeted Data Poisoning for Reinforcement Learningcited by 7
  14. 2023RLocator: Reinforcement Learning for Bug Localizationcited by 7
  15. 2024Structural Generalization in Autonomous Cyber Incident Response with Message-Passing Neural Networks and Reinforcement Learningcited by 7
  16. 2025DRMD: Deep Reinforcement Learning for Malware Detection under Concept Driftcited by 7
  17. 2021Network Defense is Not a Gamecited by 6
  18. 2024Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guaranteecited by 6
  19. 2025A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomycited by 6
  20. 2021Incorporating Deception into CyberBattleSim for Autonomous Defensecited by 5
  21. 2021Automating Privilege Escalation with Deep Reinforcement Learningcited by 5
  22. 2022Learning Security Strategies through Game Play and Optimal Stoppingcited by 5
  23. 2022DETERRENT: Detecting Trojans using Reinforcement Learningcited by 5
  24. 2024Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learningcited by 5
  25. 2025Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantizationcited by 5
  26. 2026CSLE: A Reinforcement Learning Platform for Autonomous Security Managementcited by 5
  27. 2021Constraints Satisfiability Driven Reinforcement Learning for Autonomous Cyber Defensecited by 4
  28. 2021Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspectivecited by 4
  29. 2022Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Reviewcited by 4
  30. 2025Reinforcement Learning with Physics-Informed Symbolic Program Priors for Zero-Shot Wireless Indoor Navigationcited by 4
  31. 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecuritycited by 4
  32. 2021Reinforcement Learning for Industrial Control Network Cyber Security Orchestrationcited by 3
  33. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  34. 2021Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDScited by 3
  35. 2021Application of Deep Reinforcement Learning to Payment Fraudcited by 3
  36. 2023Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertaintiescited by 3
  37. 2023Training Automated Defense Strategies Using Graph-based Cyber Attack Simulationscited by 3
  38. 2023The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learningcited by 3

50 most cited of 92 All 92 (JSON)