# CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection

> CEBin combines code embeddings to shortlist candidate functions with pairwise comparison to detect similar binary code, including vulnerable functions.

- Authors: Hao Wang; Zeyu Gao; Chao Zhang; Mingyang Sun; Yuchen Zhou; Han Qiu; Xi Xiao
- Venue: arXiv
- Status: Preprint
- Publication / posting date: February 29, 2024
- Date source: First arXiv submission date.
- First arXiv submission: February 29, 2024
- arXiv: 2402.18818
- Reviewed arXiv version: v1
- Sources reviewed: 2026-11-06
- Added to the catalog: 2026-11-06
- Original paper: <https://arxiv.org/abs/2402.18818>
- PDF: <https://arxiv.org/pdf/2402.18818>
- Code: <https://github.com/Hustcw/CEBin>
- BibTeX: <https://awesomerea.org/papers/citations/paper-cebin.bib>
- Catalog page: <https://awesomerea.org/papers/paper-cebin/>
- Record JSON: <https://awesomerea.org/papers/paper-cebin/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-cebin/index.md)

## Scope and limitations

The paper evaluates CEBin on three binary code similarity datasets and a large-scale benchmark for 1-day vulnerability detection. It reports finding similar functions among millions of candidates in seconds, with 85.46% recall on the vulnerability task.

## Why it fits this collection

It studies large-scale binary code similarity detection and its use in finding functions similar to vulnerable code.

## Publication and review notes

The date and venue come from the arXiv record.

## Primary sources

1. <https://arxiv.org/abs/2402.18818>

## About this entry

Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.