VulEPEDE: A Function-Level Vulnerability Detection Method via Enhanced Positional Encoding and Dependency Embedding

As software complexity increases, integrating vulnerability detection becomes essential to ensure the security and integrity of modern systems. Traditional static and dynamic analysis methods face limitations in efficiency and accuracy, particularly for large-scale vulnerability detection, while existing deep learning methods struggle to fully capture structural information and dependencies in code, leading to incomplete identification of vulnerabilities. In this paper, we propose VulEPEDE, an innovative function-level vulnerability detection method. VulEPEDE leverages Program Dependency Graphs (PDG) to represent function code and constructs a Vulnerability Semantic Dependency Graph (VSDG) using slicing techniques, introducing function parameter nodes as slicing candidates to capture more comprehensive vulnerability trigger chains. It integrates two core modules: the Enhanced Positional Encoding (EPE) module and the Dependency Embedding (DE) module. The EPE module combines node attribute encoding with positional encoding using a Transformer and multi-head attention mechanism to capture complex features and contextual semantics of code, while the DE module learns dependency embeddings between code nodes through convolutional neural networks. We evaluate VulEPEDE on three widely used datasets, comparing its performance against state-of-the-art deep learning-based methods. Experimental results demonstrate that VulEPEDE outperforms the best baseline methods by 1.66%, 17.54%, and 28.96% in F1-score across the three datasets, with considerable computational efficiency.