# Towards an Automated Pipeline for Detecting and Classifying Malware through Machine Learning

arXiv: https://arxiv.org/abs/2106.05625 · HTML: https://mlsanity.com/paper/2106.05625/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [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)
- [A Survey of Machine Learning Methods and Challenges for Windows Malware Classification](https://mlsanity.com/paper/2006.09271/) (2020)
- [Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification](https://mlsanity.com/paper/1511.04317/) (2015)
- [AVClass2: Massive Malware Tag Extraction from AV Labels](https://mlsanity.com/paper/2006.10615/) (2020)
- [History of malware](https://mlsanity.com/paper/1302.5392/) (2013)
- [Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique](https://mlsanity.com/paper/1910.10958/) (2019)
- [Automatic Malware Description via Attribute Tagging and Similarity Embedding](https://mlsanity.com/paper/1905.06262/) (2019)
- [Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security](https://mlsanity.com/paper/2005.01800/) (2020)

## Built on by (most cited first, 6 of 6)

- [EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers](https://mlsanity.com/paper/2506.05074/) (2025)
- [MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels](https://mlsanity.com/paper/2111.15031/) (2021)
- [Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance](https://mlsanity.com/paper/2307.14657/) (2023)
- [Semantic Preprocessing for LLM-based Malware Analysis](https://mlsanity.com/paper/2506.12113/) (2025)
- [Efficient Malware Detection with Optimized Learning on High-Dimensional Features](https://mlsanity.com/paper/2506.17309/) (2025)
- [MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware](https://mlsanity.com/paper/2309.01350/) (2023)

## Abstract (arXiv)

The constant growth in the number of malware - software or code fragment potentially harmful for computers and information networks - and the use of sophisticated evasion and obfuscation techniques have seriously hindered classic signature-based approaches. On the other hand, malware detection systems based on machine learning techniques started offering a promising alternative to standard approaches, drastically reducing analysis time and turning out to be more robust against evasion and obfuscation techniques. In this paper, we propose a malware taxonomic classification pipeline able to classify Windows Portable Executable files (PEs). Given an input PE sample, it is first classified as either malicious or benign. If malicious, the pipeline further analyzes it in order to establish its threat type, family, and behavior(s). We tested the proposed pipeline on the open source dataset EMBER, containing approximately 1 million PE samples, analyzed through static analysis. Obtained malware detection results are comparable to other academic works in the current state of art and, in addition, we provide an in-depth classification of malicious samples. Models used in the pipeline provides interpretable results which can help security analysts in better understanding decisions taken by the automated pipeline.