REVIEW 4 cited by
CybORG: An Autonomous Cyber Operations Research Gym
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Autonomous Cyber Operations (ACO) involves the consideration of blue team (defender) and red team (attacker) decision-making models in adversarial scenarios. To support the application of machine learning algorithms to solve this problem, and to encourage such practitioners to attend to problems in the ACO setting, a suitable gym (toolkit for experiments) is necessary. We introduce CybORG, a work-in-progress gym for ACO research. Driven by the need to efficiently support reinforcement learning to train adversarial decision-making models through simulation and emulation, our design differs from prior related work. Our early evaluation provides some evidence that CybORG is appropriate for our purpose and may provide a basis for advancing ACO research towards practical applications.
Forward citations
Cited by 4 Pith papers
-
Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms
By tracking per-host ground-truth states, the authors measure how often each CAGE Challenge 2 action actually changes a host's state, finding that top agents waste many actions and that decoys correlate with fewer suc...
-
Training RL Agents for Multi-Objective Network Defense Tasks
Diverse, dynamically ordered training tasks make network-defense RL agents generalize to unseen attacks better than single-task training.
-
Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
LLM cyber-defense policies, obtained by prompt engineering alone, can be behavior-cloned into a 64,910-parameter RL agent that matches a heavily trained PPO baseline in the CybORG simulator.
-
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
A narrative survey of multi-agent reinforcement learning for cyber defense, reviewing game-theoretic models, cyber gyms, and applications, concluding MARL is promising but faces scalability and simulation-to-real tran...
Discussion (0). Sign in to comment.