Radio frequency fingerprint recognition method based on prior information

Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.

With the development of the Internet of Things, more and more devices need to be connected to the network. Wireless networks are open, and the network security of devices faces major challenges. The problem of device identification is particularly important. Some traditional signal source marking methods have greatly reduced reliability. The traditional authentication methods mainly rely on software addresses. The radio frequency(RF) fingerprint technology is an emerging device authentication technology, which identifies the device based on the inherent hardware defects of the device itself. The RF fingerprints reflect the inherent characteristics of the device and are difficult to tamper with. In this paper, we proposed an environment invariant feature extraction algorithm based on the cumulative distance. The experiments show that the algorithm proposed in this paper has good robustness. Within the allowable error range, the extracted RF fingerprint features of the same signal in different frequencies, different receivers, and different environments are invariant.KeywordsMarking methodsRF fingerprintI/Q data

Aiming at the actual application scenario of radio frequency (RF) fingerprint identification, a low-cost, small-volume embedded system for specific identification of RF devices is designed and implemented, and the improved VGG-16 model is used to extract the RF fingerprint features of RF signals. The hardware of the system consists of two parts: the RF Signal Acquisition Module and the RF Fingerprint Identification Module. The RF Signal Acquisition Module uses Zedboard as the main control board, and the AD9361 chip is the RF signal receiving circuit; the RF Fingerprint Identification Module uses FPGA to accelerate the deep learning algorithm, and displays the identification results on the host computer. Using 6 wireless routers of the same model for testing, the system has an identification accuracy rate of 96% for wireless routers in the actual electromagnetic environment, and the real-time identification feedback time is about 6 seconds, which has a certain practical application value.

Radio frequency (RF) fingerprinting technology has been developed as a unique method for maintaining security based on physical layer characteristics. In this paper, we propose the RF fingerprinting by extracting the parameter characteristics such as information dimension, constellation feature, and phase noise spectrum in the transmitted information when applied to the universal software radio peripheral (USRP) software-defined radio (SDR) platform. To achieve a great performance improvement of classification, not only the traditional support vector machine (SVM) classifier, but also the machine-based integrated classifier bagged tree and the adaptive weighting algorithm weighted k-nearest neighbor (KNN) are both discussed. It is demonstrated that the proposed method achieves good classification performance under different signal-to-noise ratios (SNR).

Device identification based on radio frequency fingerprinting is widely used to improve the security of Internet of Things systems. However, noise and acquisition inconsistencies in raw radio frequency signals can affect the effectiveness of classification, identification and authentication algorithms used to distinguish Bluetooth devices. This study investigates how the RF signal preprocessing techniques affect the performance of a support vector machine classifier based on radio frequency fingerprinting. Four options derived from an RF signal preprocessing technique are evaluated, each of which is applied to the raw radio frequency signals in an attempt to improve the consistency between signals emitted by the same Bluetooth device. Experiments conducted on raw Bluetooth signals from twentyfour smartphone radios from two public databases of RF signals show that selecting an appropriate RF signal preprocessing approach can significantly improve the effectiveness of a support vector machine classifier-based algorithm used to discriminate Bluetooth devices.

Device authentication for 5G terminals via Radio Frequency fingerprints

In this paper, we present a measurement analysis of machine learning-based Radio Frequency Fingerprinting (RFFP) techniques with respect to channel power and carrier frequency offset characteristics using custom Software-Defined Radio (SDR) hardware evaluated across different ambient temperature values. RFFP models have been demonstrated to classify transmitters based on features within the transmitted signal, although to validate these models when deployed in real-time applications, they must be invariant to changes in ambient temperature. Several papers have mentioned the potential impact of ambient temperature on RFFP classification performance, but to the best of the author’s knowledge, none have directly confirmed this assertion. In this work, we employ an Orthogonal Frequency Division Multiplexing (OFDM) waveform implemented using a custom SDR testbed to apply Analysis of Variance (ANOVA) and Design of Experiments (DOE) concepts to measure the effects of temperature, transmitted power, and center frequency on overthe-air machine learning data sets across a range of operating temperatures to assess performance. RFFP models based on these features are trained using over-the-air data sets at nominal temperatures, and the models are tested to see their performance when the radios operate at different temperatures. The results show that the RFFP classification models trained at 77°<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> (25°<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i>) are not temperature-invariant at 63°<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> (17°<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i>) and 95°<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> (35°<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i>) and require new methods to maintain high classification accuracy.

Wireless Physical Layer Identification (WPLI) aims at identifying or classifying authorized devices based on the unique Radio Frequency Fingerprints (RFFs) to guarantee physical-layer security between each individual device. WPLI is a promising technique in the security area of Internet of Things (IoT), which avoids the burden of conventional software-level security in IoT on the computation capability, on-chip storage and power consumption of devices. Moreover, WPLI directly extracts the RFFs from radio frequency signals at physical layer, which breaks the software-level security barriers of heterogeneous device networks in IoT. However, in the existing works in WPLI, a general work focusing on theoretical characterization of performance WPLI systems in IoT is still in absence. In this work, we evaluate the performance of WPLI in IoT by thoroughly characterizing user capacity of WPLI system as the metric. We utilize the concept of IoT by combining multiple devices to represent one user's secure identity to increase the user capacity of WPLI system. Based on the information-theoretic tool we proposed, the changes in user capacity of WPLI is characterized over typical real-world scenarios according to different number of devices assigned for one user. Various field experiments on classification performance of a practical multi- user-multi-device WPLI system with are conducted to validate the user capacity characterization for each scenario.

Smart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart grids, cybersecurity of IoT is paramount. To this end, use of cryptographic security methods is prevalent in existing IoT. Non-cryptographic methods such as radio frequency fingerprinting (RFF) have been on the horizon for a few decades but are limited to academic research or military interest. RFF is a physical layer security feature that leverages hardware impairments in radios of IoT devices for classification and rogue device detection. The article discusses the potential of RFF in wireless communication of IoT devices to augment the cybersecurity of smart grids. The characteristics of a deep learning (DL)-aided RFF system are presented. Subsequently, a deployment framework of RFF for smart grids is presented with implementation and regulatory aspects. The article culminates with a discussion of existing challenges and potential research directions for maturation of RFF.

Radar specific emitter identification (SEI) is the process of uniquely identifying an individual emitter from the same class of radars by their individual properties that arise from hardware imperfections. However, it is challenging or perhaps impossible to generate globally unique emitter identifiers by using SEI techniques alone, due to the increasing number of radars and the subtle differences in their signal properties. We therefore introduce a multitier radar emitter identification concept that includes radar function identification, model identification, and SEI. The combination of function identifiers, model identifiers, and specific emitter identifiers could generate globally unique radar emitter identifiers. In this article, we propose to use the radio frequency (RF) features extracted from time domain transient signals for radar model identification. The RF features include the duration, maximum derivative, skewness, kurtosis, mean, variance, fractal dimension, Shannon entropy, and polynomial coefficients of the normalized energy trajectory of a transient signal, as well as the area under the trajectory curve. We propose three RF fingerprints for radar model identification, each consisting of a predetermined subset of the features. The performance of the RF fingerprints was evaluated by using five classification algorithms with two radar datasets. Our results show attractive performance with respect to hetero-model radar identification. In particular, for the hetero-model radar dataset, the pair of the all-features (AF) fingerprint and gradient boosting algorithm achieved 91.9, 90.25, and 91.1% classification accuracy for three, four, and five emitters, respectively. On this basis, we conclude that the proposed AF fingerprint could be applied directly to radar model identification by training the gradient boosting algorithm using a dataset with many radars per model.

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.

With the development of cognitive radio, more and more radio frequency devices can dynamically switch radio frequency parameters. The reliability of the traditional method of marking the radio signal source in the communication band is greatly reduced. This paper proposes a radio frequency fingerprint feature extraction method based on the accumulated distance of I/Q data components. The RF raw I/Q sample data is collected through the distributed electromagnetic spectrum detection network. The cumulative I/Q distance is extracted from the I/Q sample data collected by multiple detection nodes. The weight distribution of the I/Q components is calculated. Based on I/Q sample data estimation cumulative I/Q distance and reception signal strength parameters, the received signal strength (RSS)-I/Q distance characteristic curve and the corresponding dataset are constructed. The radio frequency fingerprint characteristic curve of the target radio device is established. The optimized Sigmod mathematical model is used as a target function. The RSS-I/Q distance data is set as a sample. The normalized dataset is fitted according to the target function. The parameter values of the optimized sigmod mathematical model are obtained by estimating the fitted curve. The estimated value is taken as the eigenvalue matrix, which is the radio frequency fingerprint characteristic parameter of the target radio equipment. The RF fingerprint feature extraction method proposed in this paper can comprehensively analyze the subtle features of radio equipment from the perspective of signal source. The experiments show that the RF fingerprint feature parameters extracted from the same signal source in different frequency bands, different environments (cement ground, asphalt road, grass, riverside, indoor, etc.), and different temperature and humidity parameters are invariant within a certain error range. The radio frequency fingerprint feature parameters extracted in different signal sources have good distinction in the same frequency band, the same environment, and the same temperature and humidity.

In the radio observation based on the large single antenna, in order to reduce the interference of navigation signal L 2 from observation of the red-shifted H I spectral line at L-band, we used the an auxiliary channel and normalized LMS algorithm, and proposed new evaluating indicators deduced by theoretical method. Finally, we verified the suppression performance and the indicators' utility by multiple simulations and statistical method.

Future wireless communications are made up of different wireless technologies. In such a scenario, cognitive and cooperative principles create a promising framework for the interaction of these systems. The opportunistic behavior of cognitive radio (CR) provides an efficient use of radio spectrum and makes wireless network setup easier. However more and more frequently, CR features are exploited by malicious attacks, e.g., denial-of-service (DoS). This paper introduces active radio frequency fingerprinting (RFF) with double application scenario. CRs could encapsulate common-control-channel (CCC) information in an existing channel using active RFF and avoiding any additional or dedicated link. On the other hand, a node inside a network could use the same technique to exchange a public key during the setup of secure communication. Results indicate how the active RFF aims to a valuable technique for cognitive radio manager (CRM) framework facilitating data exchange between CRs without any dedicated channel or additional radio resource.

The rapid growth in wireless technology has revolutionized the way of living but at the same time, raising security concerns of unauthorized access of spectrum, both military and commercial sectors. The subject of Radio Frequency (RF) fingerprinting has got special attention in recent years. Researchers proposed various datasets of radio signals of different types of devices (drones, cell phones, IoT, and Radar). However, presently there is no freely available dataset on walkie-talkies/commercial radios. To fill out the void, we present an innovative dataset including more than 2700 radio signals captured from 27 radios located in an indoor multipath environment. This dataset can enhance the security of the communication channels by providing the possibility to analyse and detect any unauthorized source of transmission. Furthermore, we also propose two innovative deep learning models named Light Weight 1DCNN and Light Weight Bivariate 1DCNN, for efficient data processing and learning patterns from the complex dataset of radio signals.