论文标题
$μ$ vuldeepecker：一种基于深度学习的多类漏洞检测系统
$μ$VulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection
论文作者
论文摘要
细颗粒软件漏洞检测是一个重要且具有挑战性的问题。理想情况下，检测系统（或检测器）不仅应该能够检测程序是否包含漏洞，而且还应能够确定所讨论的漏洞的类型。基于深度学习的现有脆弱性检测方法可以检测漏洞的存在（即解决二进制分类或检测问题），但无法查明漏洞的类型（即无法解决多类分类）。在本文中，我们提出了第一个用于多类漏洞检测的基于深度学习的系统，称为$μ$ vuldeepecker。 $ $ $ $ vuldeepecker的关键见解是代码注意的概念，即使样本很小，也可以捕获可以帮助查明漏洞类型的信息。为此，我们从头开始创建一个数据集，并使用它来评估$μ$ vuldeepecker的有效性。实验结果表明，$μ$ vuldeepecker对于多类脆弱性检测有效，并且可容纳控制依赖性（数据依赖性除外）可导致更高的检测能力。
Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed $μ$VulDeePecker. The key insight underlying $μ$VulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of $μ$VulDeePecker. Experimental results show that $μ$VulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities.
