Deep Learning-Driven Vulnerability Detection Models for Software Security

Software vulnerability detection is essential for system security, but traditional methods lack efficiency and accuracy. This study proposes a deep learning model combining CNN-BiLSTM-Attention architecture to analyze code structure and semantics, along with multi-dimensional feature extraction. Testing on 93,476 samples achieved 94.2% accuracy, 0.936 F1 score, and 82ms average detection time. The model demonstrated 93.2% effectiveness in enterprise-level validation. This automated approach significantly improves detection efficiency and reduces manual code review workload.