In this work, we conduct a comprehensive study on the robustness of domain
generation algorithm (DGA) classifiers. We implement 32 white-box attacks, 19
of which are very effective and induce a false-negative rate (FNR) of $\approx$
100\% on unhardened classifiers. To defend the classifiers, we evaluate
different hardening approaches and propose a novel training scheme that
leverages adversarial latent space vectors and discretized adversarial domains
to significantly improve robustness. In our study, we highlight a pitfall to
avoid when hardening classifiers and uncover training biases that can be easily
exploited by attackers to bypass detection, but which can be mitigated by
adversarial training (AT). In our study, we do not observe any trade-off
between robustness and performance, on the contrary, hardening improves a
classifier's detection performance for known and unknown DGAs. We implement all
attacks and defenses discussed in this paper as a standalone library, which we
make publicly available to facilitate hardening of DGA classifiers:
this https URL

该研究综合探讨了域生成算法（DGA）分类器的鲁棒性，提出了一种基于对抗潜在空间向量和离散化对抗域的新的训练方案，大幅度提高了鲁棒性，并发现了可利用的训练偏差和规避困境。研究结果显示，在鲁棒性和性能之间不存在任何权衡，相反，硬化可以提高分类器对已知和未知 DGA 的检测性能。

走向鲁棒的域生成算法分类

Towards Robust Domain Generation Algorithm Classification

Hardening cyber physical assets is both crucial and labor-intensive.
Recently, Machine Learning (ML) in general and Reinforcement Learning RL) more
specifically has shown great promise to automate tasks that otherwise would
require significant human insight/intelligence. The development of autonomous
RL agents requires a suitable training environment that allows us to quickly
evaluate various alternatives, in particular how to arrange training scenarios
that pit attackers and defenders against each other. CyberBattleSim is a
training environment that supports the training of red agents, i.e., attackers.
We added the capability to train blue agents, i.e., defenders. The paper
describes our changes and reports on the results we obtained when training blue
agents, either in isolation or jointly with red agents. Our results show that
training a blue agent does lead to stronger defenses against attacks. In
particular, training a blue agent jointly with a red agent increases the blue
agent's capability to thwart sophisticated red agents.

本文介绍了一个名为 CyberBattleSim 的 RL 训练环境，其支持红方攻击者的训练，并引入了蓝方防御者的训练。实验表明，与红方攻击者同时训练的蓝方防御者能够更好地应对复杂的攻击手段。