The literature and the news regularly report cases of exploiting Universal Serial Bus (USB) devices as attack tools for malware injections and private data exfiltration. To protect against such attacks, security researchers proposed different solutions to verify the identity of a USB device via side-channel information (e.g., timing or electromagnetic emission). However, such solutions often make strong assumptions on the measurement (e.g., electromagnetic interference-free area around the device), on a device’s state (e.g., only at the boot or during specific actions), or are limited to one particular type of USB device (e.g., flash drive or input devices).In this paper, we present PowerID, a novel method to fingerprint USB peripherals based on their power consumption. PowerID analyzes the power traces from a peripheral to infer its identity and properties. We evaluate the effectiveness of our method on an extensive power trace dataset collected from 82 USB peripherals, including 35 models and 8 types. Our experimental results show that PowerID accurately recognizes a peripheral type, model, activity, and identity.@en

Motivated by the trend of realizing full screens on devices such as smartphones, in this work we propose through-screen sensing with visible light for the application of fingertip air-writing. The system can recognize handwritten digits with under-screen photodiodes as the receiver. The key idea is to recognize the weak light reflected by the finger when the finger writes the digits on top of a screen. The proposed air-writing system has immunity to scene changes because it has a fixed screen light source. However, the screen is a double-edged sword as both a signal source and a noise source. We propose a data preprocessing method to reduce the interference of the screen as a noise source. We design an embedded deep learning model, a customized model ConvRNN, to model the spatial and temporal patterns in the dynamic and weak reflected signal for air-writing digits recognition. The evaluation results show that our through-screen fingertip air-writing system with visible light can achieve accuracy up to 91%. Results further show that the size of the customized ConvRNN model can be reduced by 94% with less than a 10% drop in performance.

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