TouchAccess: Unlock IoT Devices on Touching by Leveraging Human-Induced EM Emanations

Internet of Things (IoT) devices play essential roles in both industry and daily scenarios. However, unlike smartphones and computers, IoT devices typically lack conventional user interfaces (UIs) such as keyboards and touchscreens. It renders the traditional user authentication designs, e.g., PINs and patterns, inapplicable. In this paper, we propose
TouchAccess
that enables users to unlock an arbitrary IoT device by applying a simple touch. Our design is motivated by the key observation that IoT devices unavoidably generate electromagnetic emanations (EMM) while they are functioning. When the user touches the device, it causes time-varying coupling between those two and generates unique EMMs. Our feasibility studies further reveal that these
human-induced EMMs
are distinct and strongly correlated with the circuitry properties of the user and the device, but are susceptible to environmental EM noises, thus lowering the authentication accuracy. To address this challenge, we develop signal processing techniques with a Siamese network learning scheme that clears the ambient electromagnetic (EM) noises, extracts robust signal features, and builds noise-resistant classifiers, enabling users to be correctly recognized. A significant advantage of
TouchAccess
is that it requires only a low-cost analog-to-digital converter (ADC) to sense the EM signal. We implement
TouchAccess
on commercial off-the-shelf (COTS) IoT devices, which vary significantly in terms of UIs, sizes, and hardware designs. The performance evaluations show that
TouchAccess
achieves an average authentication accuracy as high as 97.85%. Given three login attempts, the accuracy is boosted to 99.2%.