# AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora

arXiv: https://arxiv.org/abs/2306.06228 · HTML: https://mlsanity.com/paper/2306.06228/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters](https://mlsanity.com/paper/2010.10392/) (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)
- [Survey of Machine Learning Techniques for Malware Analysis](https://mlsanity.com/paper/1710.08189/) (2017)
- [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)
- [MOTIF: A Large Malware Reference Dataset with Ground Truth Family Labels](https://mlsanity.com/paper/2111.15031/) (2021)
- [Rank-1 Similarity Matrix Decomposition For Modeling Changes in Antivirus Consensus Through Time](https://mlsanity.com/paper/2201.00757/) (2021)
- [Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing](https://mlsanity.com/paper/1512.00442/) (2015)
- [A New Burrows Wheeler Transform Markov Distance](https://mlsanity.com/paper/1912.13046/) (2019)
- [Attribute Adaptive Margin Softmax Loss using Privileged Information](https://mlsanity.com/paper/2009.01972/) (2020)

## Built on by (most cited first, 5 of 5)

- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [DFIR-Metric: A Benchmark Dataset for Evaluating Large Language Models in Digital Forensics and Incident Response](https://mlsanity.com/paper/2505.19973/) (2025)
- [Zipf-Gramming: Scaling Byte N-Grams Up to Production Sized Malware Corpora](https://mlsanity.com/paper/2511.13808/) (2025)
- [Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques](https://mlsanity.com/paper/2507.13629/) (2025)

## Abstract (arXiv)

When investigating a malicious file, searching for related files is a common task that malware analysts must perform. Given that production malware corpora may contain over a billion files and consume petabytes of storage, many feature extraction and similarity search approaches are computationally infeasible. Our work explores the potential of antivirus (AV) scan data as a scalable source of features for malware. This is possible because AV scan reports are widely available through services such as VirusTotal and are ~100x smaller than the average malware sample. The information within an AV scan report is abundant with information and can indicate a malicious file's family, behavior, target operating system, and many other characteristics. We introduce AVScan2Vec, a language model trained to comprehend the semantics of AV scan data. AVScan2Vec ingests AV scan data for a malicious file and outputs a meaningful vector representation. AVScan2Vec vectors are ~3 to 85x smaller than popular alternatives in use today, enabling faster vector comparisons and lower memory usage. By incorporating Dynamic Continuous Indexing, we show that nearest-neighbor queries on AVScan2Vec vectors can scale to even the largest malware production datasets. We also demonstrate that AVScan2Vec vectors are superior to other leading malware feature vector representations across nearly all classification, clustering, and nearest-neighbor lookup algorithms that we evaluated.