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20242026
most citedA Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing

11 citations · 30 across the 7 of their papers we have counts for

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cs.CR2025

ByteShield: Adversarially Robust End-to-End Malware Detection through Byte Masking

Daniel Gibert, Felip Manyà

Research has proven that end-to-end malware detectors are vulnerable to adversarial attacks. In response, the research community has proposed defenses based on randomized and (de)r…

cs.CR2025

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection

Daniel Pulido-Cortázar, Daniel Gibert, Felip Manyà

Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examp…