Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years, the rapid development of computer software has led to numerous security problems, particularly software vulnerabilities. These flaws can cause significant harm to users’ privacy and property. Current security defect detection technology relies on manual or professional reasoning, leading to missed detection and high false detection rates. Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes, reducing missed alarms and false alarms. So, this project aims to study Java source code defect detection methods for defects like null pointer reference exception, XSS (Transform), and Structured Query Language (SQL) injection. Also, the project uses open-source Javalang to translate the Java source code, conducts a deep search on the AST to obtain the empty syntax feature library, and converts the Java source code into a dependency graph. The feature vector is then used as the learning target for the neural network. Four types of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), and Attention Mechanism + Bidirectional LSTM, are used to investigate various code defects, including blank pointer reference exception, XSS, and SQL injection defects. Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition, verifying the correctness of the method.