一句话：提出行为图模型VulBG，用函数间全局行为信息增强基于深度学习的漏洞检测，在真实数据集上提升多个基线模型性能。
软件漏洞威胁网络安全，自动漏洞检测需求增长。现有基于深度学习的漏洞检测虽能自动提取源码特征，但在真实数据上准确率下降，且通常局限于单个函数，无法利用函数间信息。本文提出行为图模型，用于抽取函数的抽象行为并刻画函数间关系，并基于该模型设计框架VulBG，将全局行为信息融入现有深度学习检测模型。作者选取TextCNN、ASTGRU、CodeBERT、Devign、VulCNN等基线，在平衡的FFMpeg+Qemu和不平衡的Chrome+Debian真实数据集上评估。结果表明VulBG能让所有基线发现更多真实漏洞，整体提升检测性能。
Software vulnerabilities have posed huge threats to the cyberspace security, and there is an increasing demand for automated vulnerability detection (VD). In recent years, deep learning-based (DL-based) vulnerability detection systems have been proposed for the purpose of automatic feature extraction from source code. Although these methods can achieve ideal performance on synthetic datasets, the accuracy drops a lot when detecting real-world vulnerability datasets. Moreover, these approaches limit their scopes within a single function, being not able to leverage the information between functions. In this paper, we attempt to extract the function's abstract behaviors, figure out the relationships between functions, and use this global information to assist DL-based VD to achieve higher performance. To this end, we build a Behavior Graph Model and use it to design a novel framework, namely VulBG. To examine the ability of our constructed Behavior Graph Model, we choose several existing DL-based VD models (e.g., TextCNN, ASTGRU, CodeBERT, Devign, and VulCNN) as our baseline models and conduct evaluations on two real-world datasets: the balanced $\text{FFMpeg}+\text{Qemu}$ dataset and the unbalanced $\text{Chrome} +\text{Debian}$ dataset. Experimental results indicate that VulBG enables all baseline models to detect more real vulnerabilities, thus improving the overall detection performance.
@inproceedings{YuanLFWZLLJ23,
title = {Enhancing Deep Learning-based Vulnerability Detection by Building Behavior Graph Model},
author = {Bin Yuan and Yifan Lu and Yilin Fang and Yueming Wu and Deqing Zou and Zhen Li and Zhi Li and Hai Jin},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00190}
}