Multi-type Vulnerability Detection with Staged Feature Fusion and Group Data Balance

With the progress of software technology, vulnerability detection is more important in software security. The mainstream method lacks discriminability and explainability as it uses graph neural network to fuse features simultaneously. Meanwhile only showing the presence of vulnerabilities limits its usefulness. Therefore, we propose BMAAVD, a Vulnerability Detection framework based on Bilateral Masks Aggregate Attention mechanism, including a staged fusion based on feature level with adaptive structures. It treats detection as a multi-classification task and outputs classification information to improve usefulness. BMAAVD performs well on both synthetic and real datasets, achieving an average increase of 7.61 % and 14.82% in F1 score, and the group bagging training strategy we promoted improves the model's performance in data imbalance.