Steady State RF Fingerprinting for Identity Verification: One Class Classifier versus Customized Ensemble

Kroon, Barnard; Bergin, Susan; Kennedy, Irwin O.; Zamora, Georgina O'Mahony

doi:10.1007/978-3-642-17080-5_22

Cited by 13 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They compared the performance of one-class classifiers (OCC) and customized ensemble classifiers. However, the true negative rate and the true positive rate can not simultaneously reach a high level [23].…”

Section: Fingerprint Classification and Identificationmentioning

confidence: 99%

Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

Chen¹,

Peng²,

Hu³

et al. 2020

Preprint

View full text Add to dashboard Cite

With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A USRP receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

show abstract

Section: Fingerprint Classification and Identificationmentioning

confidence: 99%

Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

Chen¹,

Peng²,

Hu³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The FB technique extracts higher-order cumulants (HOC) and analyzed the features for each modulation scheme [5]. As a decision method, the modulation method is predicted by applying machine learning techniques such as SVM [6]. Wenwu et al [18] improves classification performance by constructing a deep belief neural network (DBN) based on a new feature parameter as the sixth-order cumulants of the extracted signal.…”

Section: Related Workmentioning

confidence: 99%

“…The expert features identify the signal type by finding repeated fundamental frequencies [4], [5]. Therefore, the machine learning techniques, such as support vector machine (SVM) and knearest neighbor (KNN), classify the modulation schemes using a combination of the extracted features [6]. However, the aforementioned methods degrade the classification accuracy due to the dynamic channel environment [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic Modulation Classification With Blind OFDM Parameter Estimation

Park

Han

2021

IEEE Access

View full text Add to dashboard Cite

Automatic modulation classification (AMC) is an essential factor in dynamic spectrum access to fulfill the spectrum demand of 5G wireless communications for achieving high data rate and low latency. Many deep learning (DL)-based AMC methods have achieved improved accuracy performance for classifying analog modulation schemes, single-carrier-based modulation schemes, and multi-carrier signals using several DL architectures such as the convolutional neural network (CNN) and long-short term memory (LSTM). However, most conventional DL-based AMC methods have confused the orthogonal frequency multiplexing division (OFDM)-based signals with different OFDM useful symbol lengths. To resolve the issue, we propose a CNN model operating on the fast Fourier transformation window banks (FWB) to extract the useful symbol length in OFDM, which represent the identification of each OFDMbased wireless communication technology. After extracting the OFDM useful symbol length, we propose a DL-based AMC system combined with FWB and in-phase and quadrature phase (IQ) signals to classify the OFDM symbol length and single-carrier modulation schemes simultaneously. Furthermore, we explore the constraints of the FWB parameters according to the length and the FFT size of the OFDM signal to achieve good classification accuracy through the experiment. We constructed a dataset by generating OFDM signals of different lengths while changing the FFT size in a fixed bandwidth and by selecting only quadrature amplitude modulation (QAM) schemes from RadioML2016.10a. Experimental results show the improved performance of the classification accuracy by about 30% over conventional classifiers in additive white Gaussian noise, synchronization impairments, and fading environments.

show abstract

“…SVM-based ID verification, using RF fingerprints drawn from the preambles of the Random Access Channel (RACH) waveforms, of five 3GPP UMTS mobile radios is presented in [45]. For ID verification, SVM is employed using both a single and ensemble-based approach.…”

Section: Related Workmentioning

confidence: 99%

“…The flaw of radio classification has led to the proposal of a "one-to-one" comparison known as radio identity (ID) verification (a.k.a., authentication) [15], [30], [45]- [49], [51]- [54]. In radio ID verification the RF fingerprint(s) of the unknown radio are compared only to the stored reference model associated with the presented digital ID [49].…”

Section: Introductionmentioning

confidence: 99%

Pre-print: Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM

Reising,

Cancelleri,

Loveless

et al. 2020

Preprint

View full text Add to dashboard Cite

It is estimated that the number of IoT devices will reach 75 billion in the next five years. Most of those currently, and to be deployed, lack sufficient security to protect themselves and their networks from attack by malicious IoT devices that masquerade as authorized devices to circumvent digital authentication approaches. This work presents a PHY layer IoT authentication approach capable of addressing this critical security need through the use of feature reduced Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates 100%: (i) authorized ID verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB, and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.

show abstract

Steady State RF Fingerprinting for Identity Verification: One Class Classifier versus Customized Ensemble

Cited by 13 publications

References 7 publications

Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

Deep Learning-Based Automatic Modulation Classification With Blind OFDM Parameter Estimation

Pre-print: Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM

Contact Info

Product

Resources

About