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Towards Robust Domain Generation Algorithm Classification
April 10, 2024, 4:10 a.m. | Arthur Drichel, Marc Meyer, Ulrike Meyer
cs.CR updates on arXiv.org arxiv.org
Abstract: 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 …
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