2021/10/14 by Nguyen, Van-Anh, Dai Quoc Nguyen, Van Nguyen +8 · 9 citations
Computer Science · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information and Cyber Security #Machine Learning (cs.LG) #Software Engineering Research #Software Reliability and Analysis Research
paper · pdf · doi:10.48550/arxiv.2110.07317
openalex publication_date 2021/10/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks. It, however, is also a challenging step that requires specialized expertise in security and code representation. To this end, we aim to develop a general, practical, and programming language-independent model capable of running on various source codes and libraries without difficulty. Therefore, we consider vulnerability detection as an inductive text classification problem and propose ReGVD, a simple yet effective graph neural network-based model for the problem. In particular, ReGVD views each raw source code as a flat sequence of tokens to build a graph, wherein node features are initialized by only the token embedding layer of a pre-trained programming language (PL) model. ReGVD then leverages residual connection among GNN layers and examines a mixture of graph-level sum and max poolings to return a graph embedding for the source code. ReGVD outperforms the existing state-of-the-art models and obtains the highest accuracy on the real-world benchmark dataset from CodeXGLUE for vulnerability detection. Our code is available at: \urlhttps://github.com/daiquocnguyen/GNN-ReGVD.