Machine Learning Transferability for Malware Detection
Malware continues to be a predominant operational risk for organizations, especially when obfuscation techniques are used to evade detection. Despite the ongoing efforts in the development of Machine Learning (ML) detection approaches, there is still a lack of feature compatibility in public datasets. This limits generalization when facing distribution shifts, as well as transferability to different datasets. This study evaluates the suitability of different data preprocessing approaches for the detection of Portable Executable (PE) files with ML models. The preprocessing pipeline unifies EMBERv2 (2,381-dim) features datasets, trains paired models under two training setups: EMBER + BODMAS...
| Authors | César Vieira, João Vitorino, Eva Maia et al. |
|---|---|
| Primary category | cs.CR |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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