With the rapid growth of Internet-of-Things (IoT) devices, especially in the context of smart homes, we witnessed the rise of different services aimed at providing end-users with tools for the definition of custom behaviors. Among these, If-This-Than-That (IFTTT) became the most used end-user programming tool for creating event-condition-action (ECA) rules. However, while defining such rules, end-users might expose both their smart devices and personal information to security and privacy threats. This paper presents the progress achieved in the definition of a classification model based on neural networks for the identification of possible security and privacy issues within an IFTTT applet.

Towards a Classification Model for Identifying Risky IFTTT Applets

Breve B.;Cimino G.;Deufemia V.
2021

Abstract

With the rapid growth of Internet-of-Things (IoT) devices, especially in the context of smart homes, we witnessed the rise of different services aimed at providing end-users with tools for the definition of custom behaviors. Among these, If-This-Than-That (IFTTT) became the most used end-user programming tool for creating event-condition-action (ECA) rules. However, while defining such rules, end-users might expose both their smart devices and personal information to security and privacy threats. This paper presents the progress achieved in the definition of a classification model based on neural networks for the identification of possible security and privacy issues within an IFTTT applet.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4809381
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