mlsanityData to 7 Oct 2026

Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation

arXiv 2207.12355 ↗First posted 25 Jul 2022cs.CR cs.LG stat.MLcited by 37

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Abstract (arXiv)

In this paper we explore cyber security defence, through the unification of a novel cyber security simulator with models for (causal) decision-making through optimisation. Particular attention is paid to a recently published approach: dynamic causal Bayesian optimisation (DCBO). We propose that DCBO can act as a blue agent when provided with a view of a simulated network and a causal model of how a red agent spreads within that network. To investigate how DCBO can perform optimal interventions on host nodes, in order to reduce the cost of intrusions caused by the red agent. Through this we demonstrate a complete cyber-simulation system, which we use to generate observational data for DCBO and provide numerical quantitative results which lay the foundations for future work in this space.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 25 Jul 2022.

Builds on

9 of 24 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2021CybORG: A Gym for the Development of Autonomous Cyber Agentscited by 58
  3. 2020Causal Bayesian Optimizationcited by 39
  4. 2021Network Environment Design for Autonomous Cyberdefensecited by 29
  5. 2013Probabilistic Evaluation of Sequential Plans from Causal Models with Hidden Variablescited by 23
  6. 2021Intrusion Prevention through Optimal Stoppingcited by 18
  7. 2021Dynamic Causal Bayesian Optimizationcited by 17
  8. 2021Prospective Artificial Intelligence Approaches for Active Cyber Defencecited by 11
  9. 2022Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Reviewcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 9; 2025: 8; 2026: 11.

37 papers.

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  1. 2023NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarioscited by 20
  2. 2023Automated Cyber Defence: A Reviewcited by 19
  3. 2023Inroads into Autonomous Network Defence using Explained Reinforcement Learningcited by 17
  4. 2023Out of the Cage: How Stochastic Parrots Win in Cyber Security Environmentscited by 17
  5. 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackerscited by 13
  6. 2024Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Searchcited by 12
  7. 2024Entity-based Reinforcement Learning for Autonomous Cyber Defencecited by 11
  8. 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learningcited by 9
  9. 2023Scalable Learning of Intrusion Responses through Recursive Decompositioncited by 8
  10. 2024Intrusion Tolerance for Networked Systems through Two-Level Feedback Controlcited by 8
  11. 2023Deep Reinforcement Learning for Autonomous Cyber Defence: A Surveycited by 6
  12. 2026CSLE: A Reinforcement Learning Platform for Autonomous Security Managementcited by 5
25 more
  1. 2025Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agentscited by 4
  2. 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecuritycited by 4
  3. 2023Training Automated Defense Strategies Using Graph-based Cyber Attack Simulationscited by 3
  4. 2025Learning Optimal Defender Strategies for CAGE-2 using a POMDP Modelcited by 3
  5. 2026Beyond Rewards in Reinforcement Learning for Cyber Defencecited by 3
  6. 2026Building Better Environments for Autonomous Cyber Defencecited by 3
  7. 2024Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Securitycited by 2
  8. 2025Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunitiescited by 2
  9. 2024Using Quantum Solved Deep Boltzmann Machines to Increase the Data Efficiency of RL Agentscited by 1
  10. 2024The Price of Pessimism for Automated Defensecited by 1
  11. 2024A Survey for Deep Reinforcement Learning Based Network Intrusion Detectioncited by 1
  12. 2024Machine Theory of Mind for Autonomous Cyber-Defencecited by 1
  13. 2025Less is more? Rewards in RL for Cyber Defencecited by 1
  14. 2025Training RL Agents for Multi-Objective Network Defense Taskscited by 1
  15. 2026Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Frameworkcited by 1
  16. 2026Multi-Agent Influence Diagrams to Hybrid Threat Modelingcited by 1
  17. 2024Towards Type Agnostic Cyber Defense Agentscited by 0
  18. 2025Graphical Models for Decision-Making: Integrating Causality and Game Theorycited by 0
  19. 2025Preliminary Investigation into Uncertainty-Aware Attack Stage Classificationcited by 0
  20. 2025Online Identification of IT Systems through Active Causal Learningcited by 0
  21. 2026BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defensecited by 0
  22. 2026A Cyber Range Evaluation of Autonomous Network Incident Response Agentscited by 0
  23. 2026The MAL Simulator: Cyber Operations Simulation based on Attack & Defense Graphscited by 0
  24. 2026Efficient Nash Equilibrium Computation for Cybersecurity Gamescited by 0
  25. 2026CyberWorld: World Models for Sample-Efficient Autonomous Cyber Defensecited by 0