83 citations · 143 across the 5 of their papers we have counts for
Showing 2023 · cs.CRShow all
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cs.CR2023★ 6 cited
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
Daniel Gibert, Giulio Zizzo, Quan Le
Malware detectors based on deep learning (DL) have been shown to be susceptible to malware examples that have been deliberately manipulated in order to evade detection, a.k.a. adve…
cs.CR2023★ 20 cited
Query-Free Evasion Attacks Against Machine Learning-Based Malware Detectors with Generative Adversarial Networks
Daniel Gibert, Jordi Planes, Quan Le +1
Malware detectors based on machine learning (ML) have been shown to be susceptible to adversarial malware examples. However, current methods to generate adversarial malware example…