# Asteria: Deep Learning-based AST-Encoding for Cross-platform Binary Code Similarity Detection > ASTERIA uses a Tree-LSTM to encode function ASTs and compare binary functions across platforms. It was evaluated on function pairs and used to search IoT firmware for vulnerabilities. - Authors: Shouguo Yang; Long Cheng; Yicheng Zeng; Zhe Lang; Hongsong Zhu; Zhiqiang Shi - Venue: 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) - Status: Conference paper - Publication / posting date: June 2021 - Date source: Published date in the Crossref record for the DOI listed on arXiv. - First arXiv submission: August 13, 2021 - arXiv: 2108.06082 - Reviewed arXiv version: v1 - DOI: 10.1109/dsn48987.2021.00036 - Sources reviewed: 2026-11-06 - Added to the catalog: 2026-11-06 - Original paper: - PDF: - BibTeX: - Catalog page: - Record JSON: - Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-asteria/index.md) ## Scope and limitations The model was trained on 1,022,616 function pairs and evaluated on 95,078 pairs. Its cross-platform scope covers differing IoT architectures; a firmware search identified 75 vulnerable functions in 5,979 images. ## Why it fits this collection It studies reverse engineering through binary function similarity and vulnerability search in IoT firmware. ## Publication and review notes The DOI comes from the arXiv record, and the venue and date from Crossref. ## Primary sources 1. 2. ## 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.