An Effective Software Vulnerability Detection Method Based on Dual‐Channel Convolutional Neural Network

ABSTRACT The detection of software vulnerabilities is an essential security issue in the field of cyberspace security. However, existing detectors often suffer from two main limitations: low computational efficiency owing to high cost and poor scalability when processing large‐scale code, and inadequate feature representation capability due to their failure to capture sufficient semantic and structural properties of programs, which ultimately leads to decreased detection accuracy. To address these challenges and improve detection accuracy, we propose MCV‐DCNN, a novel software vulnerability detection method based on multi‐scale centrality‐weighted image generation and dual‐channel convolutional neural networks. The proposed MCV‐DCNN method leverages multiple node centrality metrics to transform source code into image representations, thereby capturing a more holistic view of the code's structural properties. It also employs a dual‐channel convolutional neural network architecture to independently process one image with each channel in parallel. This design mitigates information interference and redundancy between the representations during feature learning. We evaluate MCV‐DCNN method on a public dataset with 33,362 C/C++ programs. Experimental results demonstrate that it significantly improves vulnerability detection accuracy while maintaining efficiency. Furthermore, the evaluation on 26,193 real‐world functions shows that MCV‐DCNN outperforms baselines by detecting 505 additional vulnerabilities, validating its effectiveness in practical settings.