CybORG++ is an advanced toolkit for reinforcement learning research focused on network defence. Building on the CAGE 2 CybORG environment, it introduces key improvements, including enhanced debugging capabilities, refined agent implementation support, and a streamlined environment that enables faster training and easier customisation. Along with addressing several software bugs from its predecessor, CybORG++ introduces MiniCAGE, a lightweight version of CAGE 2, which improves performance dramatically, up to 1000x faster execution in parallel iterations, without sacrificing accuracy or core functionality. CybORG++ serves as a robust platform for developing and evaluating defensive agents, making it a valuable resource for advancing enterprise network defence research.
@article{arxiv.2410.16324,
title = {CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents},
author = {Harry Emerson and Liz Bates and Chris Hicks and Vasilios Mavroudis},
journal= {arXiv preprint arXiv:2410.16324},
year = {2024}
}