# Bin2vec: Learning Representations of Binary Executable Programs for Security Tasks

arXiv: https://arxiv.org/abs/2002.03388 · HTML: https://mlsanity.com/paper/2002.03388/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Pre-Training Representations of Binary Code Using Contrastive Learning](https://mlsanity.com/paper/2210.05102/) (2022)
- [Deep-Learning-based Vulnerability Detection in Binary Executables](https://mlsanity.com/paper/2212.01254/) (2022)
- [UniASM: Binary Code Similarity Detection without Fine-tuning](https://mlsanity.com/paper/2211.01144/) (2022)
- [Bi-Directional Transformers vs. word2vec: Discovering Vulnerabilities in Lifted Compiled Code](https://mlsanity.com/paper/2405.20611/) (2024)
- [Binary BPE: A Family of Cross-Platform Tokenizers for Binary Analysis](https://mlsanity.com/paper/2511.17573/) (2025)
- [Impact of Data Snooping on Deep Learning Models for Locating Vulnerabilities in Lifted Code](https://mlsanity.com/paper/2412.02048/) (2024)
- [Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?](https://mlsanity.com/paper/2412.07538/) (2024)
- [Deep Learning-based Binary Analysis for Vulnerability Detection in x86-64 Machine Code](https://mlsanity.com/paper/2601.09157/) (2026)

## Abstract (arXiv)

Tackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time-consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks - functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code-based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).