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.
@article{arxiv.2002.10667,
title = {CybORG: An Autonomous Cyber Operations Research Gym},
author = {Callum Baillie and Maxwell Standen and Jonathon Schwartz and Michael Docking and David Bowman and Junae Kim},
journal= {arXiv preprint arXiv:2002.10667},
year = {2020}
}