CybORG: An Autonomous Cyber Operations Research Gym

TL;DR

CybORG is a new simulation environment designed to facilitate research in autonomous cyber operations by supporting reinforcement learning for attacker and defender decision-making in adversarial scenarios.

Contribution

It introduces CybORG, a novel gym for ACO research that supports reinforcement learning through simulation and emulation, addressing a gap in existing tools.

Findings

CybORG is suitable for training adversarial decision models.

Early evaluation shows CybORG effectively supports ACO research.

The platform may advance practical applications in autonomous cyber defense and offense.

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.