Easily establishing pairing between Internet-of-Things (IoT) devices is important for fast deployment in many smart home scenarios. Traditional pairing methods, including passkey, QR code, and RFID, often require specific user interfaces, surface’s shape/material, or additional tags/readers. The growing number of low-resource IoT devices without an interface may not meet these requirements, which makes their pairing a challenge. On the other hand, these devices often already have sensors embedded for sensing tasks, such as inertial sensors. These sensors can be used for limited user interaction with the devices, but are not suitable for pairing on their own.
In this paper, we present UniverSense, an alternative pairing method between low-resource IoT devices with an inertial sensor and a more powerful networked device equipped with a camera. To establish pairing between them, the user moves the low-resource IoT device in front of the camera. Both the camera and the on-device sensors capture the physical motion of the low-resource device. UniverSense converts these signals into a common state-space to generate fingerprints for pairing. We conduct real-world experiments to evaluate UniverSense and it achieves an F1 score of 99.9% in experiments carried out by five participants.
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I Always Feel Like Somebody's Sensing Me! A Framework to Detect, Identify, and Localize Clandestine Wireless Sensors
The increasing ubiquity of low-cost wireless sensors has enabled users to easily deploy systems to remotely monitor and control their environments. However, this raises privacy concerns for third-party occupants, such as a hotel room guest who may be unaware of deployed clandestine sensors. Previous methods focused on specific modalities such as detecting cameras but do not provide a generalized and comprehensive method to capture arbitrary sensors which may be "spying" on a user. In this work, we propose SnoopDog, a framework to not only detect common Wi-Fi-based wireless sensors that are actively monitoring a user, but also classify and localize each device. SnoopDog works by establishing causality between patterns in observable wireless traffic and a trusted sensor in the same space, e.g., an inertial measurement unit (IMU) that captures a user's movement. Once causality is established, SnoopDog performs packet inspection to inform the user about the monitoring device. Finally, SnoopDog localizes the clandestine device in a 2D plane using a novel trial-based localization technique. We evaluated SnoopDog across several devices and various modalities and were able to detect causality for snooping devices 95.2% of the time and localize devices to a sufficiently reduced sub-space.
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- Award ID(s):
- 1705135
- PAR ID:
- 10296311
- Date Published:
- Journal Name:
- 30th USENIX Security Symposium (USENIX Security 21)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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