一句话：针对二进制相似检测的词表/OOV 与 CFG 局部结构局限，提出建模基本块长距离依赖的 RCFG2Vec。
二进制代码相似性检测是软件安全中的基础技术，可用于恶意软件家族识别、已知漏洞检测和代码抄袭检测。近年基于深度学习的 BCSD 方法表现良好，但仍有两个限制检测性能的挑战：基于 RNN、Transformer 等序列网络的方法多采用粗粒度 tokenization，导致词表过大和严重 OOV 问题；基于 CFG 的方法通常使用图卷积网络变体，只考虑局部结构信息而忽略基本块之间的长距离依赖。为此，作者提出 RCFG2Vec，从标题看其核心是在二进制代码相似性检测中显式考虑长距离依赖。给出的摘要未包含具体模型设计、评估数据集和实验结果，因此无法总结关键实证结论。
Binary code similarity detection(BCSD), as a fundamental technique in software security, has various applications, including malware family detection, known vulnerability detection and code plagiarism detection. Recent deep learning-based BCSD approaches have demonstrated promising performance. However, they face two significant challenges that limit detection performance. First, most approaches that use sequence networks (like RNN and Transformer) utilize coarse-grained tokenization methods, which results in large vocabulary size and severe out-of-vocabulary (OOV) problem. Second, CFG-based methods typically use variants of graph convolutional networks, which only consider local structural information and discard long-distance dependencies between basic blocks.
DOI 原文 ·
@inproceedings{paperbot8293,
title = {RCFG2Vec: Considering Long-Distance Dependency for Binary Code Similarity Detection},
author = {Weilong Li and Jintian Lu and Ruizhi Xiao and Pengfei Shao and Shuyuan Jin},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695070}
}