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Detecting IoT user behavior and sensitive information in encrypted IoT -app traffic

Subahi, Alanoud and Theodorakopoulos, Georgios ORCID: 2019. Detecting IoT user behavior and sensitive information in encrypted IoT -app traffic. Sensors 19 (21) , 4777. 10.3390/s19214777

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Many people use smart-home devices, also known as the Internet of Things (IoT), in their daily lives. Most IoT devices come with a companion mobile application that users need to install in their smartphone or tablet in order to control, configure, and interface with the IoT device. IoT devices send information about their users from their app directly to the IoT manufacturer's cloud; we call this the ''app-to-cloud way''. In this research, we invent a tool called IoT-app privacy inspector that can automatically infer the following from the IoT network traffic: the packet that reveals user interaction type with the IoT device via its app (e.g. login), the packets that carry sensitive Personal Identifiable Information (PII), the content type of such sensitive information (e.g. user's location). We use Random Forest classifier as a supervised machine learning algorithm to extract features from network traffic. To train and test the three different multi-class classifiers, we collect and label network traffic from different IoT devices via their apps. We obtain the following classification accuracy values for the three aforementioned types of information: 99.4%, 99.8%, and 99.8%. This tool can help IoT users take an active role in protecting their privacy.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: MDPI
ISSN: 1424-8220
Date of First Compliant Deposit: 11 November 2019
Date of Acceptance: 1 November 2019
Last Modified: 05 May 2023 10:49

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