Cell-Phone Classification: A Convolutional Neural Network Approach Exploiting Electromagnetic Emanations

In this paper, we propose a methodology to identify both the brand of a cell-phone, and the status of its camera by exploiting electromagnetic (EM) emanations. The method composes two parts: Feature extraction and Convolutional Neural Netwotk (CNN). We first extract features by averaging magnitudes of short-time Fourier transform (STFT) of the measured EM signal, which helps to reduce input dimension of the neural network, and to filter spurious emissions. The extracted features are fed into the proposed CNN, which contains two convolutional layers (followed by max-pooling layers), and four fully-connected layers. Finally, we provide experimental results which exhibit more than 99% classification accuracy for the test signals.