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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…