# Machine Learning Transferability for Malware Detection

arXiv: https://arxiv.org/abs/2603.26632 · HTML: https://mlsanity.com/paper/2603.26632/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [FLAML: A Fast and Lightweight AutoML Library](https://mlsanity.com/paper/1911.04706/) (2019)
- [Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift](https://mlsanity.com/paper/2010.03856/) (2020)
- [SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection](https://mlsanity.com/paper/2012.07634/) (2020)
- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [A Survey of Machine Learning Methods and Challenges for Windows Malware Classification](https://mlsanity.com/paper/2006.09271/) (2020)
- [The choice of scaling technique matters for classification performance](https://mlsanity.com/paper/2212.12343/) (2022)
- [ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation](https://mlsanity.com/paper/1903.05700/) (2019)
- [Automated Machine Learning for Deep Learning based Malware Detection](https://mlsanity.com/paper/2303.01679/) (2023)
- [Assessing the Effectiveness of YARA Rules for Signature-Based Malware Detection and Classification](https://mlsanity.com/paper/2111.13910/) (2021)
- [Efficient Malware Detection with Optimized Learning on High-Dimensional Features](https://mlsanity.com/paper/2506.17309/) (2025)

## 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.