This repository contains the code accompanying the paper XFL: Naming Functions in Binaries with Extreme Multi-label Learning by James Patrick-Evans, Moritz Dannehl and Johannes Kinder, which has been presented at the IEEE Symposium on Security & Privacy 2023.
The repository consists of the following components:
xfl/Partial source code of the implementation used for the paper, including full experiment data and logs.xfl-r/A refactored version of the original source code to simplify configuration and deployment. (highly recommended)lm/The Language model for generating function names from lists of tokens predicted by XFL, as described in Section VI of the paper.
RevEng.AI offers a service to generate embeddings from symbols in binaries using the reait tool.
Important
XFL-R, our refactored version of XFL, is highly recommended for conducting a scientific comparison.
Please cite the paper as
@inproceedings{oakland23-xfl,
author = {James Patrick-Evans and Moritz Dannehl and Johannes Kinder},
title = {{XFL}: Naming Functions in Binaries with Extreme Multi-label Learning},
booktitle = {Proc. IEEE Symp. Security and Privacy (S\&P)},
pages = {1677-1692},
publisher = {IEEE},
year = {2023},
doi = {10.1109/SP46215.2023.00096},
}