mlsanityData to 7 Oct 2026

Machine Learning Transferability for Malware Detection

arXiv 2603.26632 ↗First posted 27 Mar 2026cs.CR cs.AI cs.LGcited by 0

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Abstract (arXiv)

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 and EMBER + BODMAS + ERMDS. Regarding model evaluation, both EMBER + BODMAS and EMBER + BODMAS + ERMDS models are tested against TRITIUM, INFERNO and SOREL-20M. ERMDS is also used for testing for the EMBER + BODMAS setup.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 27 Mar 2026.

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References · 12

  1. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 164
  2. 2017Malware Detection by Eating a Whole EXEcited by 114
  3. 2019FLAML: A Fast and Lightweight AutoML Librarycited by 104
  4. 2020Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Driftcited by 53
  5. 2020SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detectioncited by 49
  6. 2015Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Featurescited by 48
  7. 2020A Survey of Machine Learning Methods and Challenges for Windows Malware Classificationcited by 33
  8. 2022The choice of scaling technique matters for classification performancecited by 25
  9. 2019ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentationcited by 9
  10. 2023Automated Machine Learning for Deep Learning based Malware Detectioncited by 6
  11. 2021Assessing the Effectiveness of YARA Rules for Signature-Based Malware Detection and Classificationcited by 3
  12. 2025Efficient Malware Detection with Optimized Learning on High-Dimensional Featurescited by 1

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