Construction of Graph Neural Network Vulnerability Propagation Identification Mechanism for Software Supply Chain

With the increasingly prominent software supply chain security problems, how to control the spread of vulnerabilities has become the main task of supply chain security management. This paper proposes a vulnerability propagation identification mechanism utilizing Graph Neural Networks (GNNs), delves into the mechanics of vulnerability propagation, and extracts pivotal features of propagation paths. The GNN model is trained to identify the trend of vulnerability propagation, and the real-time and accuracy of vulnerability detection is improved by combining the automatic early warning system. By optimizing the identification mechanism, such as deepening the level of graph convolutional network (GCN) and integrating self-supervised learning strategies, the accuracy and efficiency of vulnerability propagation detection are improved. This mechanism can effectively identify potential vulnerability propagation paths in the software supply chain, improve vulnerability detection and protection capabilities, and provide intelligent solutions for vulnerability management.