With the advent of the in-vehicle infotainment (IVI) systems (e.g., Android Automotive) and other portable devices (e.g., smartphones) that may be brought into a vehicle, it becomes crucial to establish a secure channel between the vehicle and an in-vehicle device or between two in-vehicle devices. Traditional pairing schemes are tedious, as they require user interaction (e.g., manually typing in a passcode or bringing the two devices close to each other). Modern vehicles, together with smartphones and many emerging Internet-of-things (IoT) devices (e.g., dashcam) are often equipped with built-in Global Positioning System (GPS) receivers. In this paper, we propose a GPS-based Key estab- lishment technique, called GPSKey, by leveraging the inherent randomness of vehicle movement. Specifically, vehicle movement changes with road ground conditions, traffic situations, and pedal operations. It thus may have rich randomness. Meanwhile, two in- vehicle GPS receivers can observe the same vehicle movement and exploit it for key establishment without requiring user interaction. We implement a prototype of GPSKey on top of off-the-shelf devices. Experimental results show that legitimate devices in the same vehicle require 1.18-minute of driving on average to establish a 128-bit key. Meanwhile, the attacker who follows or leads the victim’s vehicle is unable to infer the key.
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Do You Feel What I Hear? Enabling Autonomous IoT Device Pairing Using Different Sensor Types
Context-based pairing solutions increase the usability of IoT device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing on-board sensors (with same sensing modalities) to capture a common physical context (e.g., ambient sound via each device’s microphone). However, in a smart home scenario, it is impractical to assume that all devices will share a common sensing modality. For example, a motion detector is only equipped with an infrared sensor while Amazon Echo only has microphones. In this paper, we develop a new context-based pairing mechanism called Perceptio that uses time as the common factor across differing sensor types. By focusing on the event timing, rather than the specific event sensor data, Perceptio creates event fingerprints that can be matched across a variety of IoT devices. We propose Perceptio based on the idea that devices co-located within a physically secure boundary (e.g., single family house) can observe more events in common over time, as opposed to devices outside. Devices make use of the observed contextual information to provide entropy for Perceptio’s pairing protocol. We design and implement Perceptio, and evaluate its effectiveness as an autonomous secure pairing solution. Our implementation demonstrates the ability to sufficiently distinguish between legitimate devices (placed within the boundary) and attacker devices (placed outside) by imposing a threshold on fingerprint similarity. Perceptio demonstrates an average fingerprint similarity of 94.9% between legitimate devices while even a hypothetical impossibly well-performing attacker yields only 68.9% between itself and a valid device.
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- Award ID(s):
- 1645759
- NSF-PAR ID:
- 10082721
- Date Published:
- Journal Name:
- 39th IEEE Symposium on Security and Privacy (Oakland 2018)
- Page Range / eLocation ID:
- 836 - 852
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/sp.2018.00041
