##基于可扩展图神经网络的软件漏洞自动检测

Abstract

This paper presents a novel approach to automated software vulnerability detection using scalable graph neural networks (GNNs). Traditional vulnerability detection methods often rely on manual code review or signature-based approaches, which are labor-intensive, error-prone, and struggle to identify novel vulnerabilities. Our proposed method leverages the power of GNNs to learn complex relationships within software code represented as graphs, thereby identifying patterns indicative of vulnerabilities. The core of our approach lies in constructing a graph representation of the code, where nodes represent code elements (e.g., functions, variables, statements) and edges represent relationships between them (e.g., data flow, control flow). The GNN then learns embeddings for these nodes, capturing contextual information and vulnerability patterns. We demonstrate the effectiveness of this approach in detecting various types of software vulnerabilities. The results indicate a significant improvement in detection accuracy and efficiency compared to traditional methods. The scalability of the GNN architecture allows it to handle large and complex codebases effectively. This work contributes to the development of automated and intelligent software security solutions.