Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery

Autor:	Riccardo Focardi, Gabriele Tolomei, Alvise Rabitti, Stefano Calzavara, Mauro Conti
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Forgery applications Computer Networks and Communications Computer science Browsers Vulnerability Cross-site request forgery 02 engineering and technology Machine learning computer.software_genre Tools 03 medical and health sciences 0302 clinical medicine Software 020204 information systems 0202 electrical engineering electronic engineering information engineering Web application Leverage (statistics) Electrical and Electronic Engineering 030219 obstetrics & reproductive medicine Settore INF/01 - Informatica business.industry Supervised learning Security Social networking (online) Vulnerability detection websites cross-site scripting The Internet Artificial intelligence business Law computer
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de2ef892ceeb4d6768ddcc0d9be9466c http://hdl.handle.net/11577/3330817 Zobrazit plný text záznamu