Graph Neural Network-Based Program Vulnerability Prediction

Abstract

This paper investigates the application of Graph Neural Networks (GNNs) for program vulnerability prediction. Traditional methods for vulnerability detection often rely on static analysis and signature-based approaches, which can be limited in their ability to capture complex code relationships and identify novel vulnerabilities. This research proposes a novel framework that leverages the power of GNNs to learn structural information from program code, ultimately leading to improved vulnerability prediction accuracy. The core idea is to represent program code as a graph, where nodes represent code elements (e.g., statements, functions, variables) and edges represent relationships between them. GNNs are then trained on this graph to identify patterns indicative of vulnerabilities, such as insecure coding practices and potential attack vectors. The results demonstrate the effectiveness of this approach, showcasing the potential of GNNs to enhance vulnerability detection and risk assessment in software development. The key contributions of this work include the development of a graph-based representation for program code, the design of a tailored GNN architecture for vulnerability prediction, and the demonstration of improved prediction accuracy compared to existing methods.