直接用机器码做漏洞检测，效果比反汇编更好。
Deep Learning-based Binary Analysis for Vulnerability Detection in x86-64 Machine Code
- 用原始机器码输入模型，避开反汇编损失
- 图模型比序列模型更擅长捕捉控制流关系
- 在三种漏洞类型上均表现优异，适合安全分析
当前基于深度学习的漏洞检测多依赖反汇编后的汇编代码，本文探索直接从原始x86-64机器码中提取特征的可行性。尽管汇编语言对人类更易读，但需更复杂的模型来捕捉词级别上下文；而机器码可能支持更高效、轻量的模型，并保留反汇编过程中可能丢失的信息。本文通过两个特定的深度学习模型架构开展探索性研究，系统评估其在三种漏洞类型上的表现。结果表明，图结构模型始终优于序列模型，凸显控制流关系的重要性；且机器码包含足够信息以实现有效的漏洞发现。
原文摘要 · Abstract (English)
While much of the current research in deep learning-based vulnerability detection relies on disassembled binaries, this paper explores the feasibility of extracting features directly from raw x86-64 machine code. Although assembly language is more interpretable for humans, it requires more complex models to capture token-level context. In contrast, machine code may enable more efficient, lightweight models and preserve all information that might be lost in disassembly. This paper approaches the task of vulnerability detection through an exploratory study on two specific deep learning model architectures and aims to systematically evaluate their performance across three vulnerability types. The results demonstrate that graph-based models consistently outperform sequential models, emphasizing the importance of control flow relationships, and that machine code contains sufficient information for effective vulnerability discovery.
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