Deep Learning for Spectrum Awareness and Covert Communications via Unintended RF Emanations

Abstract

We present a deep learning-based spectrum sensing and covert communication framework for unintended (side-channel) electromagnetic emanations. Electronic devices release unintentional RF emissions (without using any RF transmitter) depending on their processing activities and these emissions can be captured at the clock frequency and its harmonics. First, we train a deep neural network, in particular, a convolutional neural network (CNN), to detect RF emanations from a microcontroller (in particular, Arduino Uno R3). Through over-the-air (OTA) experiments, we show that the CNN that is trained with the input of signals received at a software-defined radio (SDR) can reliably detect RF emanations at a range of up to 10 feet, while the performance of conventional energy detector remains limited. Second, we demonstrate how to encode RF emanations due to different programs running on a microcontroller and generate frequency shift keying (FSK) modulated signals without using any RF transmitter. The conventional scheme of quadrature demodulation can decode signals communicated at a range of up to 9 inches. On the other hand, we show that a CNN that is trained with RF emanation data collected with an SDR can decode the signals encoded over RF emanations with higher reliability and extend the communication range up to 4 feet. These capabilities are promising to support emerging systems such as Internet of Things (IoT) with novel applications including covert communications, low-power device monitoring and sensing, and energy-efficient communications.