Feb. 14, 2022, 2:11 a.m. | Limin Yang, Zhi Chen, Jacopo Cortellazzi, Feargus Pendlebury, Kevin Tu, Fabio Pierazzi, Lorenzo Cavallaro, Gang Wang

cs.LG updates on arXiv.org arxiv.org

Malware classifiers are subject to training-time exploitation due to the need
to regularly retrain using samples collected from the wild. Recent work has
demonstrated the feasibility of backdoor attacks against malware classifiers,
and yet the stealthiness of such attacks is not well understood. In this paper,
we investigate this phenomenon under the clean-label setting (i.e., attackers
do not have complete control over the training or labeling process).
Empirically, we show that existing backdoor attacks in malware classifiers are
still detectable …

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