An Overview of Radio Frequency Fingerprinting for Low-End Devices

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.

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...

Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, 2014

Radio Frequency (RF) fingerprinting is a security mechanism inspired by biological fingerprint identification systems. RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. RF fingerprinting classification is performed by selecting an “unknown” signal from the pool, generating its RF fingerprint, and using a classifier to correlate the received RF fingerprint with each profile RF fingerprint stored in the database. Unlike a human biological fingerprint, RF fingerprint of a wireless device changes with the received Signal to Noise Ratio (SNR) and varies due to mobility of the transmitter/receiver and environment. The variations in the features of RF fingerprints affect the classification results of the RF fingerprinting. This chapter evaluates the performance of the KNN and neural network classification for varying SNR. Performance analysis is performed for three scenarios that correspond to the situation, when either transmitter ...

IEEE Access, 2019

Radio frequency (RF) fingerprinting is considered as one of the promising techniques to enhance wireless security in the Internet of Things (IoT) applications. In this paper, a low-complexity RF fingerprinting method for classification of wireless IoT devices is proposed. The method is based on the energy spectrum of the transmitter turn-on transient signals from which unique characteristics of wireless devices are extracted. The number of spectral components to be used is determined through a proposed approach based on the estimated transient duration value. Transient duration estimation is achieved from the smoothed versions of the instantaneous amplitude characteristics of transmitter signals, which are obtained through a sliding window averaging method. Classification performance of the proposed spectral fingerprints is assessed using experimental data and described by a confusion matrix. The discrimination effectiveness of the spectral fingerprints is quantified by a class separability criterion and evaluated for different noise levels through Monte Carlo simulations. It is demonstrated that the proposed fingerprints outperform the classification performance of two existing fingerprints especially at low signal-to-noise ratio. Additionally, computational complexity analysis of the classifier using the proposed fingerprints is provided. INDEX TERMS Internet of Things (IoT) security, radio transmitter turn-on transient, RF fingerprinting, transient energy spectrum, wireless device identification.

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.

Wireless Personal Communications, 2020

Radio frequency fingerprinting (RFF) could provide an efficient solution to address the security issues in wireless networks. The data acquisition system constitutes an important part of RFF. In this context, this paper presents an implementation of a modular RF front end system to be used in data acquisition for RFF. Modularity of the system provides flexible implementation options to suit diverse frequency bands with different applications. Moreover, the system is able to collect data by means of any digitizer, and enable to record the data at lower frequencies. Therefore, proposed RF front end system becomes a low-cost alternative to existing devices used in data acquisition. In its implementation, Bluetooth (BT) signals were used. Initially, transients of BT signals were detected by utilizing a large number of BT devices (smartphones). From the detected transients, distinctive signal features were extracted. Then, support vector machine (SVM) and neural networks (NN) classifiers were implemented to the extracted features for evaluating the feasibility of proposed system in RFF. As a result, 96.9% and 96.5% classification accuracies on BT devices have been demonstrated for SVM and NN classifiers respectively.

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.

Node forgery or impersonation, in which legitimate cryptographic credentials are captured by an adversary, constitutes one major security threat facing wireless networks. The fact that mobile devices are prone to be compromised and reverse engineered significantly increases the risk of such attacks in which adversaries can obtain secret keys on trusted nodes and impersonate the legitimate node. One promising approach toward thwarting these attacks is through the extraction of unique fingerprints that can provide a reliable and robust means for device identification. These fingerprints can be extracted from transmitted signal by analyzing information across the protocol stack. In this paper, the first unified and comprehensive tutorial in the area of wireless device fingerprinting for security applications is presented.

2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010

Successful "cracking" of bit-level security compromises network integrity and physical layer augmentation is being investigated to improve overall security. Intra-cellular security is addressed here using device-specific RF "Distinct Native Attribute" (RF-DNA) fingerprints in a localized regional air monitor, with targeted applications including cellular networks such as the Global System for Mobile (GSM) Communications and last mile Worldwide Interoperability for Microwave Access (WiMAX) systems. Previous work demonstrated GSM intermanufacturer classification (manufacturer discrimination) using RF-DNA fingerprinting and achieved accuracies of 92% at SN R = 6 dB. These results are extended here for intramanufacturer classification (serial number discrimination). Historically, intra-manufacturer discrimination has posed the greatest challenge and RF-DNA fingerprinting has been effective with both Orthogonal Frequency Division Multiplexed (OFDM) and Direct Sequence Spread Spectrum (DSSS) network signals. Intra-manufacturer GSM results are provided here based on identical signal collection, fingerprint generation, and MDA/ML classification processes used for previous inter-manufacturer assessment. When comparing performance, the trend for GSM intra-manufacturer classification is consistent with previous work for other network-based signals and device classification is much more challenging. For classification accuracies of 80% or better, intra-manufacturer fingerprinting requires an increase of 20 − 25 dB in SN R to achieve inter-manufacturer performance.

2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2015

To increase network security and mitigate identity theft attacks, much of the research is focused on traditional bit-level algorithmic. In conventional wireless networks, security issues are primarily considered above the physical layer and are usually based on bit-level algorithms to establish the identity of a legitimate wireless device. Physical layer security is a new paradigm in which features extracted from an analog signal can be used to establish the unique identity of a transmitter. Our previous research work into Radiometric fingerprinting has shown that every transmitter has a unique fingerprint owing to imperfections in the analog components present in the RF front end. However, to the best of the author's knowledge, no such example is available in the literature in which the effect of radio channel on Radiometric fingerprint is evaluated. This paper presents the simulation and experimental results for radiometric fingerprinting under an indoor varying radio channel. Contrary to popular assumption, it was found that the fingerprinting accuracy is little affected in an indoor channel environment.