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GuardiaML: Machine Learning-Assisted Dynamic Information Flow Control Host Publication: 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2019) Authors: A. Luis Scull Pupo, J. Nicolay, K. Efthymiadis, A. Nowé, C. De Roover and E. Gonzalez Boix Publisher: IEEE Publication Date: Mar. 2019 Number of Pages: 5 ISBN: 978-1-7281-0591-8
Abstract: Developing JavaScript and web applications with confidentiality and integrity guarantees is challenging. Information flow control enables the enforcement of such guarantees. However, the integration of this technique into software tools used by developers in their workflow is missing. In this paper we present GuardiaML, a machine learning-assisted dynamic information flow control tool for JavaScript web applications. GuardiaML enables developers to detect unwanted information flow from sensitive sources to public sinks. It can handle the DOM and interaction with internal and external libraries and services. Because the specification of sources and sinks can be tedious, GuardiaML assists in this process by suggesting the tagging of sources and sinks via a machine learning component. External Link.
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