mlsanityData to 14 Oct 2026

Machine Learning for Detecting Malware in PE Files

arXiv 2212.13988 ↗First posted 12 Dec 2022cs.CR cs.LGcited by 2

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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.

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

5 of 17 references.

References · 5

  1. 2018EMBER: An Open Dataset for Training Static PE Malware Machine Learning Modelscited by 165
  2. 2017Malware Detection by Eating a Whole EXEcited by 115
  3. 2015Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Featurescited by 48
  4. 2018Fully Understanding the Hashing Trickcited by 11
  5. 2018A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Contentcited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Explainable Malware Detection with Tailored Logic Explained Networkscited by 1
  2. 2025Efficient Malware Detection with Optimized Learning on High-Dimensional Featurescited by 1