Radio Identification Device Research Articles

RF fingerprint measurements for the identification of devices in wireless communication networks based on feature reduction and subspace transformation

This document proposes a radiofrequency (RF) fingerprinting strategy for the proper identification of wireless devices in mobile and wireless networks. The proposed identification methods are based on the extraction of the preamble RF fingerprint of a device and its comparison with a set of already known device RF fingerprints. The identification method combines techniques for feature reduction such as Principal Component Analysis (PCA) and Partial Least Squares regression (PLS), both based on subspace transformation, along with a similarity-based analysis. In this work, a complete procedure for RF fingerprint data extraction and analysis is provided. In addition, some experimentation with commercial Wi-Fi devices is carried out for the methods validation.

Radiofrequency identification of wireless devices based on RF fingerprinting

A radiofrequency (RF) fingerprinting strategy for the proper identification of wireless devices in mobile and wireless networks is proposed. The main objective is the extraction of the main parameters of the RF signal of the device by means of the preamble in the communication in the wireless network. A complete procedure for RF fingerprint data extraction is provided, and some experimentation is conducted for its validation.

A Nonparametric Method for Detecting Unintended Electromagnetic Emissions

Unintended electromagnetic emissions of RF transceivers are signals that are emitted by all RF devices and often lie within the noise band. The identification of such signals becomes an important issue as the identification, localization, and recognition of malicious wireless devices could result in a passive means to render such devices useless. In this paper, we present a new nonparametric method to distinguish signals from noise based in a morphological trimming of the data complemented with an analysis of the shape of the sequence of curves in which the data series can be decomposed. The method takes its tools from the mathematical morphology and multivariate statistics. The good performance of this methodology is illustrated with a set of real data coming from three small RF transceivers such as two-way talk radios, which are commonly used in improvised explosive devices.

On physical-layer identification of wireless devices

Physical-layer device identification aims at identifying wireless devices during radio communication by exploiting unique characteristics of their analog (radio) circuitry. This work systematizes the existing knowledge on this topic in order to enable a better understanding of device identification, its implications on the analysis and design of security solutions in wireless networks and possible applications. We therefore present a systematic review of physical-layer identification systems and provide a summary of current state-of-the-art techniques. We further present a classification of attacks and discuss the feasibility, limitations, and implications in selected applications. We also highlight issues that are still open and need to be addressed in future work.

Improved Radiometric Identification of Wireless Devices Using MIMO Transmission

Authenticating wireless devices based on features of their transmitted waveform has become a topic of considerable interest. Recent work in this arena has shown that examination of features in a modulated waveform can lead to highly precise identification of 802.11 devices. This paper experimentally demonstrates improved identification accuracy and reduced sensitivity to identification parameters when such techniques are applied to the multiple transmitters within multiple-input multiple-output devices. It further studies an information theoretic technique for determining which radiometric features are most effective in discriminating devices and analyzes the identification performance as a function of the number of features used. Finally, because some radiometric features are sensitive to environmental conditions and, therefore, drift with time, the analysis explores the impact of radiometric feature drift both on feature prioritization and on identification performance.

AN AUTOMATED SYSTEM FOR THE MEASUREMENT OF MASS AND IDENTIFICATION OF BIRDS AT PERCHES

Studies on small wintering birds often monitor body mass variation over days or seasons. Typically, birds are weighed manually or data are obtained using an electronic balance which displays the bird's mass during visits to a feeder. The problem with these techniques is that they require the manipulation of the animals and/or are labor intensive. We built an automated system which combines a transponder-based radio identification device (RID) and an electronic balance fitted with a weighing perch. This setup permits daily mass variation in birds to be monitored automatically without human presence. The system provides reliable estimates of inert masses (r2 = 1). We also used it to monitor bird masses in an aviary. For five days birds were caught periodically and weighed manually to measure their “real” masses for comparison with the system's estimates. Again a strong relationship was found between calculated and real masses (r2 = 0.92 and r2 = 0.89 using two different balances). This system offers great promise and could be adapted to many forms of field work.