CybORG: An Autonomous Cyber Operations Research Gym
This provides a domain-specific tool for researchers in cyber security to develop adversarial decision-making models, but it is incremental as it builds on prior work.
The authors tackled the need for a toolkit to apply machine learning in Autonomous Cyber Operations by introducing CybORG, a gym for research, with early evaluation suggesting it is appropriate for advancing the field.
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.