Moat: Adaptive Inside/Outside Detection System for Smart Homes

[wang:insideout]

Chixiang Wang, Weijia He, Timothy Pierson, and David Kotz. Moat: Adaptive Inside/Outside Detection System for Smart Homes. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, volume 8, number 4, article 157, 31 pages. ACM, September 2024. doi:10.1145/3699751. ©Copyright ACM.

Abstract:

Smart-home technology is now pervasive, demanding increased attention to the security of the devices and the privacy of the home's residents. To assist residents in making security and privacy decisions - e.g., whether to allow a new device to connect to the network, or whether to be alarmed when an unknown device is discovered - it helps to know whether the device is inside the home, or outside.

In this paper we present MOAT, a system that leverages Wi-Fi sniffers to analyze the physical properties of a device's wireless transmissions to infer whether that device is located inside or outside of a home. MOAT can adaptively self-update to accommodate changes in the home indoor environment to ensure robust long-term performance. Notably, MOAT does not require prior knowledge of the home's layout or cooperation from target devices, and is easy to install and configure.

We evaluated MOAT in four different homes with 21 diverse commercial smart devices and achieved an overall balanced accuracy rate of up to 95.6%. Our novel periodic adaptation technique allowed our approach to maintain high accuracy even after rearranging furniture in the home. MOAT is a practical and efficient first step for monitoring and managing devices in a smart home.

Citable with [BibTeX]

Projects: [splice]

Keywords: [iot] [privacy] [security] [sensors] [wifi]

Available from the publisher: [DOI]

Available from the author: [bib] [pdf]
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[Kotz research]