CybORG：一个自主的网络作战研究健身房
CybORG: An Autonomous Cyber Operations Research Gym
摘要：
自主网络作战（ACO）涉及在对抗场景中考虑蓝队（防御者）和红队（攻击者）的决策模型。为了支持应用机器学习算法来解决这个问题，并鼓励这些从业者在ACO环境中解决问题，需要一个合适的健身房（实验工具包）。我们介绍CybORG，一个正在进行的健身房，用于ACO研究。由于需要有效地支持强化学习，通过模拟和仿真来训练对抗性决策模型，我们的设计与以前的相关工作不同。我们的早期评估提供了一些证据，CybORG是适合我们的目的，并可能提供一个基础，推进ACO研究走向实际应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
