一句话：提出轻量级框架，在无需重量级二进制分析的情况下，为大规模 stripped binaries 重赋描述性函数名。
软件常以不含调试信息的 stripped binaries 发布，函数名缺失给逆向工程带来困难。本文研究为函数重赋描述性名称的问题，并将其视为数据驱动的预测任务，因此需要足够大规模且多样化的数据才能获得有说服力的结论。然而，已有研究依赖重量级二进制分析，只能在小规模数据集上开展，难以处理体量大、规模大的二进制。为此，论文提出一种轻量级框架，降低分析开销，使函数名重赋能够面向大规模 stripped binaries；其目标是在保持可扩展性的同时支持更广泛的数据驱动研究。
Software in the wild is usually released as stripped binaries that contain no debug information (e.g., function names). This paper studies the issue of reassigning descriptive names for functions to help facilitate reverse engineering. Since the essence of this issue is a data-driven prediction task, persuasive research should be based on sufficiently large-scale and diverse data. However, prior studies can only be based on small-scale datasets because their techniques suffer from heavyweight binary analysis, making them powerless in the face of big-size and large-scale binaries.
DOI 原文 ·
@inproceedings{paperbot6660,
title = {A lightweight framework for function name reassignment based on large-scale stripped binaries},
author = {Han Gao and Shaoyin Cheng and Yinxing Xue and Weiming Zhang},
booktitle = {ISSTA},
year = {2021},
doi = {10.1145/3460319.3464804}
}