一句话：提出 BinAug，通过低成本输入修复提升基于 DNN 的二进制代码相似性分析的稳定性与可用性。
二进制代码相似性分析（BCSA）是软件安全、逆向工程与重工程的基础构件。已有研究将深度神经网络（DNN）用于度量二进制代码相似性，虽取得鼓舞性结果，但由于 DNN 的不稳定性与黑盒特性，工业界尚未广泛部署。本文提出 BinAug，其核心思路是通过低成本输入修复来增强二进制相似性分析。标题与摘要显示，该工作针对 DNN-based BCSA 的上述局限，试图以输入修复而非仅依赖模型内部解释来提升分析质量。所提供的摘要未给出具体修复机制、数据集与量化实验结果，因此无法进一步总结其实现细节与评估结论。
Binary code similarity analysis (BCSA) is a fundamental building block for various software security, reverse engineering, and re-engineering applications. Existing research has applied deep neural networks (DNNs) to measure the similarity between binary code, following the major breakthrough of DNNs in processing media data like images. Despite the encouraging results of DNN-based BCSA, it is however not widely deployed in the industry due to the instability and the black-box nature of DNNs.
DOI 原文 ·
@inproceedings{paperbot11431,
title = {BinAug: Enhancing Binary Similarity Analysis with Low-Cost Input Repairing},
author = {Wai Kin Wong and Huaijin Wang and Zongjie Li and Shuai Wang},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3623328}
}