标题： 基于边缘感知图注意力网络的C/C++可解释漏洞检测 显示英文标题

标题： Explainable Vulnerability Detection in C/C++ Using Edge-Aware Graph Attention Networks

摘要： 检测源代码中的安全漏洞仍然具有挑战性，特别是在现实世界的数据集中由于漏洞函数的代表性不足而导致的类别不平衡问题。现有的基于学习的方法通常优化召回率，导致误报率较高，并在开发工作流中降低了可用性。此外，许多方法缺乏可解释性，限制了它们在安全工作流中的集成。本文提出了 ExplainVulD，一种用于 C/C++ 代码漏洞检测的基于图的框架。该方法构建代码属性图，并使用双通道嵌入表示节点，以捕捉语义和结构信息。这些信息通过一种边感知注意力机制进行处理，该机制结合边类型嵌入以区分程序关系。为了解决类别不平衡问题，模型使用类别加权交叉熵损失进行训练。在 ReVeal 数据集上，ExplainVulD 在 30 次独立运行中实现了 88.25% 的平均准确率和 48.23% 的 F1 分数。与 ReVeal 模型（一种先前的基于学习的方法）相比，这些结果在准确率上提高了 4.6%，在 F1 分数上提高了 16.9%。该框架还优于静态分析工具，在准确率上提高了 14.0% 到 14.1%，在 F1 分数上提高了 132.2% 到 201.2%。除了改进的检测性能外，ExplainVulD 通过识别每个函数中最关键的代码区域来生成可解释的输出，支持安全优先级处理中的透明度和信任。 显示英文摘要

摘要： Detecting security vulnerabilities in source code remains challenging, particularly due to class imbalance in real-world datasets where vulnerable functions are under-represented. Existing learning-based methods often optimise for recall, leading to high false positive rates and reduced usability in development workflows. Furthermore, many approaches lack explainability, limiting their integration into security workflows. This paper presents ExplainVulD, a graph-based framework for vulnerability detection in C/C++ code. The method constructs Code Property Graphs and represents nodes using dual-channel embeddings that capture both semantic and structural information. These are processed by an edge-aware attention mechanism that incorporates edge-type embeddings to distinguish among program relations. To address class imbalance, the model is trained using class-weighted cross-entropy loss. ExplainVulD achieves a mean accuracy of 88.25 percent and an F1 score of 48.23 percent across 30 independent runs on the ReVeal dataset. These results represent relative improvements of 4.6 percent in accuracy and 16.9 percent in F1 score compared to the ReVeal model, a prior learning-based method. The framework also outperforms static analysis tools, with relative gains of 14.0 to 14.1 percent in accuracy and 132.2 to 201.2 percent in F1 score. Beyond improved detection performance, ExplainVulD produces explainable outputs by identifying the most influential code regions within each function, supporting transparency and trust in security triage.