VulSCP: Automated Code Vulnerability Detection via Sequential Convolution and Parallel Attention Mechanism

As software applications grow increasingly large and complex, traditional code vulnerability detection methods struggle with performance and efficiency. Although code visualization-based algorithms have demonstrated effectiveness in capturing sparse features and complex workflows in large-scale source code, their capacity to extract global semantic information and intricate long-range dependencies remains limited. Recent large language model (LLM)-based approaches have shown promising accuracy by leveraging rich contextual information, but their high computational cost often limits practical efficiency. To address these challenges, we propose VulSCP, a new framework that integrates sequential convolution with a parallel attention mechanism. Specifically, VulSCP first constructs a semantically weighted graph from the source code, then employs sequential convolution to extract local vulnerability-related features, and finally enhances the global feature representation through parallel attention. Experimental results on large-scale C/C++ function-level datasets show that VulSCP achieves an accuracy of 85.14% and a false positive rate of 17.25%, outperforming the best baseline in accuracy by 1.73 percentage points and reducing the false positive rate by 3.38 percentage points. Moreover, while maintaining high detection accuracy, VulSCP achieves a low average inference time of 1.89 s per sample, showing favorable efficiency compared with the evaluated LLM-based methods. These results suggest that VulSCP is a promising approach for vulnerability detection in large and complex software systems, offering a favorable balance between accuracy and efficiency. The source code of VulSCP is publicly available at https://github.com/Hwzx-ZeL/VulSCP.