# Machine Learning for Detecting Malware in PE Files

arXiv: https://arxiv.org/abs/2212.13988 · HTML: https://mlsanity.com/paper/2212.13988/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 12 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [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)
- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [Fully Understanding the Hashing Trick](https://mlsanity.com/paper/1805.08539/) (2018)
- [A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content](https://mlsanity.com/paper/1804.05020/) (2018)

## Built on by (most cited first, 2 of 2)

- [Explainable Malware Detection with Tailored Logic Explained Networks](https://mlsanity.com/paper/2405.03009/) (2024)
- [Efficient Malware Detection with Optimized Learning on High-Dimensional Features](https://mlsanity.com/paper/2506.17309/) (2025)

## Abstract (arXiv)

The increasing number of sophisticated malware poses a major cybersecurity threat. Portable executable (PE) files are a common vector for such malware. In this work we review and evaluate machine learning-based PE malware detection techniques. Using a large benchmark dataset, we evaluate features of PE files using the most common machine learning techniques to detect malware.