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.
Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery / Calzavara, Stefano; Conti, Mauro; Focardi, Riccardo; Rabitti, Alvise; Tolomei, Gabriele. - In: IEEE SECURITY & PRIVACY. - ISSN 1540-7993. - (2020), pp. 2-10. [10.1109/MSEC.2019.2961649]
Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery
Mauro Conti;Gabriele Tolomei
2020
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.File allegati a questo prodotto
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