Mitch is a language-agnostic tool, based on a new CSRF detection heuristic, which operates without having access to the source code of the web application to test. This makes it suited to analyze both open- and closed-source web applications, potentially developed using different pro- gramming languages (Section V).
In this paper we present Mitch, the first machine learning solution for the black-box detection of CSRF vulnerabilities.
In this paper we present Mitch, the first machine learning solution for the black-box detection of CSRF vulnerabilities. At the core of Mitch there is an ...
Our solution outperforms existing detection heuristics proposed in the literature, allowing us to identify 35 new CSRF vulnerabilities on 20 major websites and ...
This paper presents Mitch, the first machine learning solution for the black-box detection of CSRF vulnerabilities, an automated detector of sensitive HTTP ...
Mitch: A Machine Learning Approach to the Black-Box Detection of CSRF Vulnerabilities. Stefano Calzavara, Mauro Conti, Riccardo Focardi, Alvise Rabitti, ...
This paper presents Mitch, the first machine learning solution for the black-box detection of CSRF vulnerabilities, an automated detector of sensitive HTTP ...
In order to create Mitch, the first machine learning solution for the black-box detection of Cross-Site Request Forgery (CSRF) vulnerabilities, we used our ...
Aug 17, 2022 · We use our methodology in the design of Mitch, the first ML solution for the black-box detection of Cross-Site Request Forgery (CSRF) ...
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Mitch: A Machine Learning Approach to the Black-Box Detection of CSRF Vulnerabilities. IEEEEuroS&P 2019. Cross-Site Request Forgery (CSRF) is one of the ...