CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents

Autor:	Emerson, Harry, Bates, Liz, Hicks, Chris, Mavroudis, Vasilios
Rok vydání:	2024
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 3 figures and included appendix
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.16324 Zobrazit plný text záznamu View this record from Arxiv