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.
2021-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.