# Clustering Malware at Scale: A First Full-Benchmark Study

arXiv: https://arxiv.org/abs/2511.23198 · HTML: https://mlsanity.com/paper/2511.23198/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 28 Nov 2025.

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

- [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)
- [SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection](https://mlsanity.com/paper/2012.07634/) (2020)
- [Classification and Online Clustering of Zero-Day Malware](https://mlsanity.com/paper/2305.00605/) (2023)
- [Online Clustering of Known and Emerging Malware Families](https://mlsanity.com/paper/2405.03298/) (2024)

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning](https://mlsanity.com/paper/2608.02348/) (2026)

## Abstract (arXiv)

Recent years have shown that malware attacks still happen with high frequency. Malware experts seek to categorize and classify incoming samples to confirm their trustworthiness or prove their maliciousness. One of the ways in which groups of malware samples can be identified is through malware clustering. Despite the efforts of the community, malware clustering which incorporates benign samples has been under-explored. Moreover, despite the availability of larger public benchmark malware datasets, malware clustering studies have avoided fully utilizing these datasets in their experiments, often resorting to small datasets with only a few families. Additionally, the current state-of-the-art solutions for malware clustering remain unclear. In our study, we evaluate malware clustering quality and establish the state-of-the-art on Bodmas and Ember - two large public benchmark malware datasets. Ours is the first study of malware clustering performed on whole malware benchmark datasets. Additionally, we extend the malware clustering task by incorporating benign samples. Our results indicate that incorporating benign samples does not significantly degrade clustering quality. We find that there are differences in the quality of the created clusters between Ember and Bodmas, as well as a private industry dataset. Contrary to popular opinion, our top clustering performers are K-Means and BIRCH, with DBSCAN and HAC falling behind.