Portability of an RF fingerprint of a wireless transmitter

Sakarya University Journal of Computer and Information Sciences

The Internet of Things (IoT) concept is widely used today. As IoT becomes more widely adopted, the number of devices communicating wirelessly (using various communication standards) grows. Due to resource constraints, customized security measures are not possible on IoT devices. As a result, security is becoming increasingly important in IoT. It is proposed in this study to use the physical layer properties of wireless signals as an effective method of increasing IoT security. According to the literature, radio frequency (RF) fingerprinting (RFF) techniques are used as an additional layer of security for wireless devices. To prevent spoofing or spoofing attacks, unique fingerprints appear to be used to identify wireless devices for security purposes (due to manufacturing defects in the devices' analog components). To overcome the difficulties in RFF, different parts of the transmitted signals (transient/preamble/steady-state) are used. This review provides an overview of the mos...

arXiv (Cornell University), 2021

We present a new RF fingerprinting technique for wireless emitters that is based on a simple, easily and efficiently retrainable Ridge Regression (RR) classifier. The RR learns to identify devices using bursts of waveform samples, conveniently transformed and preprocessed by delay-loop reservoirs. Deep delay Loop Reservoir Computing (DLR) is our processing architecture that supports general machine learning algorithms on resource-constrained devices by leveraging delay-loop reservoir computing (RC) and innovative architectures of loop trees. In prior work, we trained and evaluated DLR using high SNR device emissions in clean channels. We here demonstrate how to use DLR for IoT authentication by performing RF-based Specific Emitter Identification (SEI), even in the presence of fading channels and heavy in-band jamming by leveraging a matched filter (MF) extension, dubbed MF-DLR. We show that the MF processing improves the SEI performance of RR without the RC transformation (MF-RR), but the MF-DLR is more robust and applicable for addressing signatures beyond waveform transients (e.g. turn-on).

Micro-Electronics and Telecommunication Engineering

Radio fingerprint aims to provide an authentication and authorization for wireless devices by identification. It improves the security of wireless network at the physical layer which also enhance the time complexity by eliminating the need of authentication at higher layers. Artificial intelligence (AI) has proven its significant improvement in the radio fingerprinting, especially a multidimensional mapping feature of AI enhances the accuracy of radio fingerprint. However, the wireless network channel reduces the accuracy of radio fingerprinting algorithm. Hence, it is important to study the data classification of radio fingerprinting with lager amount of dataset collected from various wireless channels and quantitative analysis of effect of wireless network on radio fingerprint. In this research, 8 terabytes of data are collected from various wireless devices having similar radio frequency circuit. This dataset is used to analyze the effect of wireless network on radio authentication. Experimental results show the effect of wireless network channel on the classification accuracy of artificial intelligence algorithm which varies from 98 to 8% for the collected dataset. It is also observed that balancing I/Q data increases fingerprinting accuracy.

2008 IEEE 68th Vehicular Technology Conference, 2008

We present a novel technique for radio transmitter identification based on frequency domain characteristics. Our technique detects the unique features imbued in a signal as it passes through a transmit chain. We are the first to propose the use of discriminatory classifiers based on steady state spectral features. In laboratory experiments, we achieve 97% accuracy at 30dB SNR and 66% accuracy at 0dB SNR based on eight identical Universal Software Radio Peripherals (USRP) transmitters. Our technique can be implemented using today's low cost high-volume receivers and requires no manual performance tuning.

Security is one of the primary concerns when designing wireless networks. Along detecting user identity, it is also important to detect the devices at the hardware level. The trivial identity create-and-discard process at higher layers of the protocol stack alone is not sufficient to effectively counter security threats, such as masquerading and Sybil attacks. To counter these attacks, various radio frequency fingerprinting-based solutions are proposed for the identification of the devices. However, these approaches use expansive devices for signal capturing and rely on high sampling rates and large feature sets for analysis. In this paper, we propose a radio frequency fingerprinting-based device identification technique. The proposed technique is tested on 4G-LTE network for combined intra and inter-manufacturer device detection. It uses low-cost software defined radio to capture smartphone emissions at a lower sampling rate, using our proposed preamble threshold-based detection algorithm. The results show that our proposed technique provides classification accuracy of 95.6% at different SNR levels.

IEEE Internet of Things Journal, 2018

Radio frequency (RF) fingerprint is the inherent hardware characteristics and has been employed to classify and identify wireless devices in many Internet of Things (IoT) applications. This paper extracts novel RF fingerprint features, designs a hybrid and adaptive classification scheme adjusting to the environment conditions, and carries out extensive experiments to evaluate the performance. In particular, four modulation features, namely differential constellation trace figure (DCTF), carrier frequency offset, modulation offset and I/Q offset extracted from constellation trace figure (CTF), are employed. The feature weights under different channel conditions are calculated at the training stage. These features are combined smartly with the weights selected according to the estimated signal to noise ratio (SNR) at the classification stage. We construct a testbed using universal software radio peripheral (USRP) platform as the receiver and 54 ZigBee nodes as the candidate devices to be classified, which are the most ZigBee devices ever tested. Extensive experiments are carried out to evaluate the classification performance under different channel conditions, namely line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We then validate the robustness by carrying out the classification process 18 months after the training, which is the longest time gap. We also use a different receiver platform for classification for the first time. The classification error rate is as low as 0.048 in LOS scenario, and 0.1105 even when a different receiver is used for classification 18 months after the training. Our hybrid classification scheme has thus been demonstrated effective in classifying a large amount of ZigBee devices.

Measurement, 2014

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.

Due to the advent of the Internet of Things era, the number of related wireless devices is increasing, making the abundant and complex information networks formed by communication between devices. Therefore, security and trust between devices a huge challenge. In the traditional identification method, there are identifiers such as hash-based message authentication code, key, and so on, often used to mark a message that the receiving end can verify it. However, this kind of identifiers is easy to tamper. Therefore, recently researchers address the idea that using RF fingerprint, also called radio frequency fingerprint, for identification. Our paper demonstrates a method that extracts properties and identifies each device. We achieved a high identification rate, 99.9% accuracy in our experiments where the devices communicate with Wi-Fi protocol. The proposed method can be used as a stand-alone identification feature, or for two-factor authentication.

IEEE Journal on Selected Areas in Communications, 2011

Variations in the RF chain of radio transmitters can be used as a signature to uniquely associate wireless devices with a given transmission. Previous approaches, which have varied from transient analysis to machine learning, do not provide verifiable accuracy, which is essential for admissibility of the methods in the court. Here we detail a first step toward a modelbased approach, which uses statistical models of RF transmitter components that are amenable for analysis. Algorithms based on statistical signal processing methods are developed to exploit non-linearities of wireless transmitters for the purpose of user identification in wireless systems. The decision rules are derived and their performance is analyzed. In order to establish the viability of the proposed approach, the practical variations of transmitter chain components are analyzed based on simulations, measurements and manufacturers' specifications. Results show that the proposed identification methods can be effective, even for short data records and relatively low signal-to-noise ratios, when exploiting imperfections of commercially used RF transmitters.

2017 10th International Conference on Electrical and Electronics Engineering (ELECO), 2017

In this paper, effect of sampling rate on the performance of transmitter identification system using transient-based RF fingerprints is considered. Two different existing RF fingerprinting techniques have been employed to investigate the performance of a transmitter identification system by using experimental data collected at a high sampling rate. Decimation was carried out to analyze the effect of lower sampling rates. It has been shown that transient-based RF fingerprinting methods can be effectively used for identification of wireless transmitters at low sampling rates.