# Towards usable automated detection of CPU architecture and endianness for arbitrary binary files and object code sequences

> The paper builds open datasets and tools to identify CPU architecture and endianness in binary files and object code. It evaluates classifiers on executable code sections, reporting over 98% accuracy.

- Authors: Sami Kairajärvi; Andrei Costin; Timo Hämäläinen
- Venue: Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy
- Status: Conference paper
- Publication / posting date: March 16, 2020
- Date source: Published date in the Crossref record for the DOI listed on arXiv.
- First arXiv submission: August 15, 2019
- arXiv: 1908.05459
- Reviewed arXiv version: v1
- DOI: 10.1145/3374664.3375742
- Sources reviewed: 2026-11-06
- Added to the catalog: 2026-11-06
- Original paper: <https://doi.org/10.1145/3374664.3375742>
- PDF: <https://arxiv.org/pdf/1908.05459>
- BibTeX: <https://awesomerea.org/papers/citations/paper-isadetect.bib>
- Catalog page: <https://awesomerea.org/papers/paper-isadetect/>
- Record JSON: <https://awesomerea.org/papers/paper-isadetect/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-isadetect/index.md)

## Scope and limitations

Classifier results use code sections from executable binary files.

## Why it fits this collection

Identifying the correct instruction set and endianness helps reverse-engineering tools interpret binary code accurately.

## Publication and review notes

The DOI comes from the arXiv record, and the venue and date from Crossref.

## Primary sources

1. <https://arxiv.org/abs/1908.05459>
2. <https://api.crossref.org/works/10.1145/3374664.3375742>

## 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.