VDDA: An Effective Software Vulnerability Detection Model Based on Deep Learning and Attention Mechanism

Abstract

Many of software vulnerability detection methods suffer from problems of dependent on expert experience, rough detection granularity, and incomplete syntax and semantics information on source codes. This paper proposes the VDDA, Vulnerability Detection based on Deep learning and Attention mechanism, an effective software vulnerability detection model based on deep learning and the attention mechanism. In the VDDA, deep learning technology is used to construct the underlying classifier to avoid the feature engineering of traditional machine learning techniques. The Joren slice tool combined with the code attribute graph (CPG) optimization is used to simplify the source code before it is fed to the Bidirectional Long Short-Term Memory (BLSTM) deep model. Meanwhile, the attention mechanism is employed to improve the efficiency and accuracy of vulnerability detection. Experiment results have demonstrated that the proposed VDDA model is more effective than the existing vulnerability detection methods.