一句话：提出基于语义学习的跨平台二进制漏洞搜索工具VulSeeker，以克服传统CFG方法在跨平台场景下的性能局限。
代码复用提升了软件开发效率，但也可能无意中引入漏洞。现有许多工作基于控制流图（CFG）计算代码相似性，以判断二进制函数是否包含已知漏洞，然而它们在跨平台二进制搜索中性能受到挑战。为此，本文提出VulSeeker，一个基于语义学习的跨平台二进制漏洞搜索工具，旨在克服传统CFG方法在跨平台场景下的局限性。摘要未提供具体评估结果，但工作聚焦于跨平台二进制漏洞检测的挑战。
Code reuse improves software development efficiency, however, vulnerabilities can be introduced inadvertently. Many existing works compute the code similarity based on CFGs to determine whether a binary function contains a known vulnerability. Unfortunately, their performance in cross-platform binary search is challenged.
DOI 原文 ·
@inproceedings{paperbot4762,
title = {VulSeeker: a semantic learning based vulnerability seeker for cross-platform binary},
author = {Jian Gao and Xin Yang and Ying Fu and Yu Jiang and Jiaguang Sun},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3240480}
}