CybORG: An Autonomous Cyber Operations Research Gym
arXiv:2002.10667
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.
References in corpus (5)
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Cited by in corpus (6)
- Network Environment Design for Autonomous Cyberdefense
- Deep hierarchical reinforcement agents for automated penetration testing
- CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems
- Evaluating Deception and Moving Target Defense with Network Attack Simulation
- Network Defense is Not a Game
- alurity, a toolbox for robot cybersecurity