mlsanityData to 14 Oct 2026

Autonomous Network Defence using Reinforcement Learning

arXiv 2409.18197 ↗First posted 26 Sep 2024cs.AI cs.CR cs.LGcited by 15

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

In the network security arms race, the defender is significantly disadvantaged as they need to successfully detect and counter every malicious attack. In contrast, the attacker needs to succeed only once. To level the playing field, we investigate the effectiveness of autonomous agents in a realistic network defence scenario. We first outline the problem, provide the background on reinforcement learning and detail our proposed agent design. Using a network environment simulation, with 13 hosts spanning 3 subnets, we train a novel reinforcement learning agent and show that it can reliably defend continual attacks by two advanced persistent threat (APT) red agents: one with complete knowledge of the network layout and another which must discover resources through exploration but is more general.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 15 references that are in the map. First posted on arXiv 26 Sep 2024.

Builds on

7 of 15 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  3. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  4. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  5. 2018Distributed Prioritized Experience Replaycited by 269
  6. 2019Deep Reinforcement Learning for Cyber Securitycited by 95
  7. 2021CybORG: A Gym for the Development of Autonomous Cyber Agentscited by 60

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 4; 2026: 5.

15 papers.

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  1. 2024CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agentscited by 11
  2. 2024Entity-based Reinforcement Learning for Autonomous Cyber Defencecited by 11
  3. 2025DRMD: Deep Reinforcement Learning for Malware Detection under Concept Driftcited by 7
  4. 2025Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agentscited by 7
  5. 2024Designing Robust Cyber-Defense Agents with Evolving Behavior Treescited by 4
  6. 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecuritycited by 4
  7. 2024Out-of-Distribution Detection for Neurosymbolic Autonomous Cyber Agentscited by 3
  8. 2025Learning Optimal Defender Strategies for CAGE-2 using a POMDP Modelcited by 3
  9. 2026Building Better Environments for Autonomous Cyber Defencecited by 3
  10. 2024The Price of Pessimism for Automated Defensecited by 1
  11. 2026Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Frameworkcited by 1
  12. 2024Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problemscited by 0
3 more
  1. 2025Strategic Cyber Defense via Reinforcement Learning-Guided Combinatorial Auctionscited by 0
  2. 2026Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agentscited by 0
  3. 2026REPLICANT: Learning Policies for Evading and Hardening Malware Detectorscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
  2. 2026A Cyber Range Evaluation of Autonomous Network Incident Response Agents
  3. 2024Towards Type Agnostic Cyber Defense Agents
  4. 2025Towards a Generalisable Cyber Defence Agent for Real-World Computer Networks
  5. 2025An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents
  6. 2025Quantitative Resilience Modeling for Autonomous Cyber Defense
  7. 2026Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response
  8. 2024Exploring reinforcement learning for incident response in autonomous military vehicles