Do You Feel What I Hear? Enabling Autonomous IoT Device Pairing Using Different Sensor Types
Do You Feel What I Hear? Enabling Autonomous IoT Device Pairing Using Different Sensor Types
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DOI：
10.1109/sp.2018.00041
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发表时间：
2018-05
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通讯作者：
Jun Han;Albert Jin Chung;M. K. Sinha;M. Harishankar;Shijia Pan;H. Noh;Pei Zhang;P. Tague
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文献类型：
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作者：
Jun Han;Albert Jin Chung;M. K. Sinha;M. Harishankar;Shijia Pan;H. Noh;Pei Zhang;P. Tague
基于上下文的配对解决方案通过消除任何人工参与配对过程来提高物联网设备配对的可用性。这可以通过使用车载传感器（具有相同的传感模式）来捕获共同的物理环境（例如，通过每个设备的麦克风捕获环境声音）。然而，在智能家居场景中，假设所有设备都共享一个共同的传感模式是不切实际的。例如，运动探测器只配备了红外传感器，而亚马逊Echo只配备了麦克风。在本文中，我们开发了一种新的基于上下文的配对机制，称为Perceptio，它使用时间作为不同传感器类型的共同因素。通过专注于事件时间，而不是特定的事件传感器数据，Perceptio创建了可以在各种物联网设备上匹配的事件指纹。我们提出的Perceptio基于这样的想法：与外部设备相比，位于物理安全边界内（例如，单户住宅）的设备可以随着时间的推移观察到更多共同的事件。设备利用观察到的上下文信息为Perceptio的配对协议提供熵。我们设计并实现了Perceptio，并评估了其作为自主安全配对解决方案的有效性。我们的实现演示了通过对指纹相似性施加阈值来充分区分合法设备（放置在边界内）和攻击设备（放置在边界外）的能力。Perceptio显示合法设备之间的平均指纹相似度为94.9%，而即使是假设的不可能表现出色的攻击者，其自身与有效设备之间的指纹相似度也只有68.9%。
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.