2020 · Conference paper
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.
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 details
- 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
The DOI comes from the arXiv record, and the venue and date from Crossref.
Sources reviewed
Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.