No abstract

The authors investigate the application of deep convolutional neural networks (CNNs) to the problem of radiometric identification, i.e. the task of authenticating wireless devices on the basis of their radio frequency (RF) emissions, which contain features directly related to the physical properties of the wireless devices. They collected digitised RF from 12 wireless devices, and used various techniques to transform the time series derived from the RF to images. A deep CNN is then applied to the images. The authors' results show that the identification performance of the combination of deep CNN with an image representation significantly outperforms conventional methods based on dissimilarity on the original time series. Moreover, a specific comparison among RF-to-image techniques show that on their datasets the wavelet-based approach outperforms other approaches, also in the presence of white Gaussian noise.

No abstract

No abstract

The correct identification of smartphones has various applications in the field of security or the fight against counterfeiting. As the level of sophistication in counterfeit electronics increases, detection procedures must become more accurate but also not destructive for the smartphone under testing. Some components of the smartphone are more likely to reveal their authenticity even without a physical inspection, since they are characterized by hardware fingerprints detectable by simply examining the data they provide. This is the case of MEMS (Micro Electro-Mechanical Systems) components like accelerometers and gyroscopes, where tiny differences and imprecisions in the manufacturing process determine unique patterns in the data output. In this paper, we present the experimental evaluation of the identification of smartphones through their built-in MEMS components. In our study, three different phones of the same model are subject to repeatable movements (composing a repeatable scenario) using an high precision robotic arm. The measurements from MEMS for each repeatable scenario are collected and analyzed. The identification algorithm is based on the extraction of the statistical features of the collected data for each scenario. The features are used in a support vector machine (SVM) classifier to identify the smartphone. The results of the evaluation are presented for different combinations of features and Inertial Measurement Unit (IMU) outputs, which show that detection accuracy of higher than 90% is achievable.