Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery
Stefano Calzavara, Mauro Conti, Riccardo Focardi, Alvise Rabitti, Gabriele Tolomei
Abstract
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.
Topics & Concepts
Leverage (statistics)Computer scienceVulnerability (computing)SoftwareComputer securityWeb siteWorld Wide WebCross-site scriptingWeb applicationArtificial intelligenceWeb application securityWeb pageWeb developmentThe InternetOperating systemWeb Application Security VulnerabilitiesAdvanced Malware Detection TechniquesSpam and Phishing Detection