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.

本文介绍了一种终端到终端的方法，用于研究攻击策略、设计防御代理并解释其运行。使用状态图可视化对抗行为，我们使用一组深入强化学习代理来训练不同任务部分并组织在浅层次结构中的防御模型，最终设计与之前工作相比性能有了实质性提高。最后，我们进行特征削弱和重要性研究来更好地研究我们代理的决策过程。

利用可解释的强化学习进入自治网络防御

Inroads into Autonomous Network Defence using Explained Reinforcement  Learning

The existence of adversarial data examples has drawn significant attention in
the deep-learning community; such data are seemingly minimally perturbed
relative to the original data, but lead to very different outputs from a
deep-learning algorithm. Although a significant body of work on developing
defensive models has been considered, most such models are heuristic and are
often vulnerable to adaptive attacks. Defensive methods that provide
theoretical robustness guarantees have been studied intensively, yet most fail
to obtain non-trivial robustness when a large-scale model and data are present.
To address these limitations, we introduce a framework that is scalable and
provides certified bounds on the norm of the input manipulation for
constructing adversarial examples. We establish a connection between robustness
against adversarial perturbation and additive random noise, and propose a
training strategy that can significantly improve the certified bounds. Our
evaluation on MNIST, CIFAR-10 and ImageNet suggests that the proposed method is
scalable to complicated models and large data sets, while providing competitive
robustness to state-of-the-art provable defense methods.

本文提出一种可扩展的用于构造对抗样本的方法，通过建立抵抗对抗扰动和加法噪声之间的联系，提出了一种训练策略，并在 MNIST 等数据集上进行了评估。