mlsanityData to 14 Oct 2026

Automated Cyber Defence: A Review

arXiv 2303.04926 ↗First posted 8 Mar 2023cs.CR cs.AIcited by 21

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Abstract (arXiv)

Within recent times, cybercriminals have curated a variety of organised and resolute cyber attacks within a range of cyber systems, leading to consequential ramifications to private and governmental institutions. Current security-based automation and orchestrations focus on automating fixed purpose and hard-coded solutions, which are easily surpassed by modern-day cyber attacks. Research within Automated Cyber Defence will allow the development and enabling intelligence response by autonomously defending networked systems through sequential decision-making agents. This article comprehensively elaborates the developments within Automated Cyber Defence through a requirement analysis divided into two sub-areas, namely, automated defence and attack agents and Autonomous Cyber Operation (ACO) Gyms. The requirement analysis allows the comparison of automated agents and highlights the importance of ACO Gyms for their continual development. The requirement analysis is also used to critique ACO Gyms with an overall aim to develop them for deploying automated agents within real-world networked systems. Relevant future challenges were addressed from the overall analysis to accelerate development within the area of Automated Cyber Defence.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 49 of its 119 references that are in the map. First posted on arXiv 8 Mar 2023.

Builds on

49 of 119 references.

References · 49

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 8; 2026: 4.

21 papers.

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  1. 2024Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defensecited by 17
  2. 2023Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learningcited by 12
  3. 2024Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Searchcited by 12
  4. 2024CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agentscited by 11
  5. 2025Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucinationcited by 10
  6. 2024Adversarial Inception Backdoor Attacks against Reinforcement Learningcited by 8
  7. 2023Deep Reinforcement Learning for Autonomous Cyber Defence: A Surveycited by 7
  8. 2024RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanationcited by 7
  9. 2025Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agentscited by 7
  10. 2024Deep Learning Model Security: Threats and Defensescited by 4
  11. 2024The Path To Autonomous Cyber Defensecited by 3
  12. 2025Learning Optimal Defender Strategies for CAGE-2 using a POMDP Modelcited by 3
9 more
  1. 2026Beyond Rewards in Reinforcement Learning for Cyber Defencecited by 3
  2. 2025Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platformscited by 2
  3. 2026ACDZero: MCTS Agent for Mastering Automated Cyber Defensecited by 2
  4. 2025Quantitative Resilience Modeling for Autonomous Cyber Defensecited by 1
  5. 2025Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applicationscited by 1
  6. 2025General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackerscited by 1
  7. 2026Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Frameworkcited by 1
  8. 2025Strategic Cyber Defense via Reinforcement Learning-Guided Combinatorial Auctionscited by 0
  9. 2026Autonomous Cyber Operations Gym for Training and Evaluating AI Models for Defencecited by 0