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

Baki Berkay Yilmaz; Elvan Mert Ugurlu; Alenka G. Zajic; Milos Prvulovic

ICASSP 2020accepted0 citations

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

Baki Berkay Yilmaz, Elvan Mert Ugurlu, Alenka G. Zajic, Milos Prvulovic

Abstract

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 is composed of two parts: Feature extraction and Convolutional Neural Network (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.

BibTeX

@inproceedings{icassp2020_cellphoneclassif,
  title = {Cell-Phone Classification: A Convolutional Neural Network Approach Exploiting Electromagnetic Emanations},
  author = {Baki Berkay Yilmaz and Elvan Mert Ugurlu and Alenka G. Zajic and Milos Prvulovic},
  booktitle = {ICASSP 2020},
  year = {2020}
}