  Computer network defence is a complicated task that has necessitated a high
degree of human involvement. However, with recent advancements in machine
learning, fully autonomous network defence is becoming increasingly plausible.
This paper introduces an end-to-end methodology for studying attack strategies,
designing defence agents and explaining their operation. First, using state
diagrams, we visualise adversarial behaviour to gain insight about potential
points of intervention and inform the design of our defensive models. We opt to
use a set of deep reinforcement learning agents trained on different parts of
the task and organised in a shallow hierarchy. Our evaluation shows that the
resulting design achieves a substantial performance improvement compared to
prior work. Finally, to better investigate the decision-making process of our
agents, we complete our analysis with a feature ablation and importance study.
