On Autonomous Agents in a Cyber Defence Environment

Autor:	Kiely, Mitchell, Bowman, David, Standen, Maxwell, Moir, Christopher
Rok vydání:	2023
Předmět:	Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	Autonomous Cyber Defence is required to respond to high-tempo cyber-attacks. To facilitate the research in this challenging area, we explore the utility of the autonomous cyber operation environments presented as part of the Cyber Autonomy Gym for Experimentation (CAGE) Challenges, with a specific focus on CAGE Challenge 2. CAGE Challenge 2 required a defensive Blue agent to defend a network from an attacking Red agent. We provide a detailed description of the this challenge and describe the approaches taken by challenge participants. From the submitted agents, we identify four classes of algorithms, namely, Single- Agent Deep Reinforcement Learning (DRL), Hierarchical DRL, Ensembles, and Non-DRL approaches. Of these classes, we found that the hierarchical DRL approach was the most capable of learning an effective cyber defensive strategy. Our analysis of the agent policies identified that different algorithms within the same class produced diverse strategies and that the strategy used by the defensive Blue agent varied depending on the strategy used by the offensive Red agent. We conclude that DRL algorithms are a suitable candidate for autonomous cyber defence applications. Comment: Presented at the 2nd Internation Workshop on Adaptive Cyber Defence, 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.07388 Zobrazit plný text záznamu View this record from Arxiv