一种融合双曲表示与欧几里得表示的源代码漏洞检测方法OA

With the increasing complexity of software systems,source code vulnerability detection has be-come a key task to maintain software security.Although various vulnerability detection methods based on deep learning were proposed,they mainly relied on a single Euclidean space perspective to extract the se-mantic features and structural features in the code representation structure,which limited their ability to detect vulnerabilities hidden deep in the code.In order to solve this limitation,VulDEHGCN,a source code vulnerability detection method fusing hyperbolic representation and euclidean representation was pro-posesed.It could embedd the source code from two different spaces,so as to mine the vulnerability char-acteristics of the source code from different perspectives,so as to achieve more accurate vulnerability de-tection.The experimental results showed that compared with the existing vulnerability detection methods,VulDEHGCN achieved significant improvement in key performance indicators such as accuracy,preci-sion,recall and F1 score.The accuracy and F1 score reached 98.93%and 96.63%respectively.Abla-tion studies also confirm the superiority of fusing code embeddings from different perspectives to further enhance vulnerability detection performance.