Vulnerability Prediction of Web Applications from Source Code Based on Machine Learning and Deep Learning: Where Are At?

With the rise of new information technologies around the world, many distributed applications and web applications have emerged, so it is important to make them secure. Despite the emphasis placed by software security experts on the need to build secure web applications, the number of new vulnerabilities found in web applications is growing. Machine Learning (ML) and Deep Learning (DL) through their vulnerability prediction approach are increasingly being offered for source code analysis, providing a powerful way to make web applications less vulnerable. Many ML- and DL-based approaches have been proposed to automatically detect, locate, and repair software vulnerabilities. Although ML-based are more effective than vulnerability analysis tools based on static source code analysis by security experts, accurately identifying types of vulnerabilities and estimations severity remains challenging. The graphical representation of source code, the best vulnerability differentiation, and the support of a large corpus of vulnerabilities are not at least. This thesis aims to study the prediction of vulnerabilities in web applications from source codes using ML and DL techniques. A comprehensive review of the literature on the different approaches proposed for the prediction of vulnerabilities of web applications will allow us to identify the current state of research and challenges in this field, thus positioning us well to make a significant contribution in the prediction of vulnerabilities of web applications using the techniques of ML and DL.