一句话：提出通过图对齐学习跨平台二进制代码嵌入，以解决现有嵌入无法跨平台对齐且缺少上下文信息的问题。
跨平台二进制分析需要一种跨平台的二进制公共表示，以便在其上执行具体分析。已有工作尝试学习反汇编二进制代码的低维数值向量表示（嵌入），并在嵌入空间中完成二进制分析。然而现有技术存在两类不足：一是仅针对单一平台，产生的嵌入无法跨平台对齐；二是未设计用于捕获反汇编二进制中丰富的上下文信息。本文（标题所示）提出利用图对齐进行表示学习，以改进跨平台二进制分析，核心在于让不同平台的二进制代码嵌入共享同一表示空间，并保留反汇编代码中的上下文信息。给出的摘要未报告具体方法细节与实验结果。
Cross-platform binary analysis requires a common representation of binaries across platforms, on which a specific analysis can be performed. Recent work proposed to learn low-dimensional, numeric vector representations (i.e., embeddings) of disassembled binary code, and perform binary analysis in the embedding space. Unfortunately, however, existing techniques fall short in that they are either (i) specific to a single platform producing embeddings not aligned across platforms, or (ii) not designed to capture the rich contextual information available in a disassembled binary.
DOI 原文 ·
@inproceedings{paperbot7396,
title = {Improving cross-platform binary analysis using representation learning via graph alignment},
author = {Geunwoo Kim and Sanghyun Hong and Michael Franz and Dokyung Song},
booktitle = {ISSTA},
year = {2022},
doi = {10.1145/3533767.3534383}
}