Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery
Stefano Calzavara, Mauro Conti, Riccardo Focardi, Alvise Rabitti, Gabriele Tolomei · IEEE Security & Privacy · 2020
We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software.