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Power Profiling Smart Home Devices
Cui, Kailai
Cui, Kailai
Abstract
In recent years, the growing market for smart home devices has raised concerns about user privacy and security. Previous works have utilized power auditing measures to infer activity of IoT devices to mitigate security and privacy threats. In this thesis, we explore the potential of extracting information from the power consumption traces of smart home devices. We present a framework that collects smart home devices’ power traces with current sensors and preprocesses them for effective inference. We collect an extensive dataset of duration > 2h from 6 devices including smart speakers, smart camera and smart display. We perform different classification tasks including device identification and action classification and present accuracy and confusion matrices for each tasks. Our analysis reveals that from devices’ running power traces, we can accurately identify the type of smart device being used with 93% accuracy and subsequently infer user behavior with on average 92% accuracy.
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Date
2023-05-01
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Computer Science