mlsanityData to 14 Oct 2026

CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents

arXiv 2410.16324 ↗First posted 18 Oct 2024cs.CR cs.AIcited by 11

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

Abstract (arXiv)

CybORG++ is an advanced toolkit for reinforcement learning research focused on network defence. Building on the CAGE 2 CybORG environment, it introduces key improvements, including enhanced debugging capabilities, refined agent implementation support, and a streamlined environment that enables faster training and easier customisation. Along with addressing several software bugs from its predecessor, CybORG++ introduces MiniCAGE, a lightweight version of CAGE 2, which improves performance dramatically, up to 1000x faster execution in parallel iterations, without sacrificing accuracy or core functionality. CybORG++ serves as a robust platform for developing and evaluating defensive agents, making it a valuable resource for advancing enterprise network defence research.

Key facts. Cited by 11 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 8 of its 11 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

8 of 11 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Autonomous Network Defence using Reinforcement Learning26 Sep

Other references · 7

  1. 2021CybORG: A Gym for the Development of Autonomous Cyber Agentscited by 60
  2. 2023On Autonomous Agents in a Cyber Defence Environmentcited by 26
  3. 2023Automated Cyber Defence: A Reviewcited by 21
  4. 2023Reward Shaping for Happier Autonomous Cyber Security Agentscited by 19
  5. 2023Inroads into Autonomous Network Defence using Explained Reinforcement Learningcited by 17
  6. 2024Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Searchcited by 12
  7. 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learningcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 8.

11 papers.

  1. 2026SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecuritycited by 4
  2. 2026Beyond Rewards in Reinforcement Learning for Cyber Defencecited by 3
  3. 2026Building Better Environments for Autonomous Cyber Defencecited by 3
  4. 2025Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applicationscited by 1
  5. 2025Reinforcement Learning for Automated Cybersecurity Penetration Testingcited by 1
  6. 2026SENTINEL-RL: Offloading Topological Reasoning from LLM Agents in the Security Operations Centercited by 1
  7. 2024Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problemscited by 0
  8. 2026NASimJax: A GPU-Accelerated Policy Learning Framework for Penetration Testingcited by 0
  9. 2026COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologiescited by 0
  10. 2026A Cyber Range Evaluation of Autonomous Network Incident Response Agentscited by 0
  11. 2026Efficient Nash Equilibrium Computation for Cybersecurity Gamescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Production-Worthy Simulation for Autonomous Cyber Operations
  2. 2026Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
  3. 2025An Attentive Graph Agent for Topology-Adaptive Cyber Defence
  4. 2026No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents
  5. 2026Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
  6. 2025Towards a Generalisable Cyber Defence Agent for Real-World Computer Networks
  7. 2024Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
  8. 2024Entity-based Reinforcement Learning for Autonomous Cyber Defence