RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context

Morge-Rollet, Louis; Roy, Frédéric Le; Jeune, Denis Le; Canaff, Charles; Gautier, Roland

doi:10.3390/s22114291

Cited by 4 publications

(4 citation statements)

References 41 publications

(89 reference statements)

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“…Siamese networks have also seen use in radio systems, shown to be effective in areas such as automatic modulation classification [37]. Finally, some research has shown promise in using Siamese networks for fingerprinting radio transmitters [38,39].…”

Section: Conv1dtransposementioning

confidence: 99%

Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting

Smailes,

Köhler,

Birnbach

et al. 2023

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

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Due to an increase in the availability of cheap off-the-shelf radio hardware, signal spoofing and replay attacks on satellite ground systems have become more accessible than ever. This is particularly a problem for legacy systems, many of which do not offer cryptographic security and cannot be patched to support novel security measures.Therefore, in this paper we explore radio transmitter fingerprinting in the context of satellite systems. We introduce the SatIQ system, proposing novel techniques for authenticating transmissions using characteristics of the transmitter hardware expressed as impairments on the downlinked radio signal. We look in particular at high sample rate fingerprinting, making device fingerprints difficult to forge without similarly high sample rate transmitting hardware, thus raising the required budget for spoofing and replay attacks. We also examine the difficulty of this approach with high levels of atmospheric noise and multipath scattering, and analyze potential solutions to this problem.We focus on the Iridium satellite constellation, for which we collected 1 705 202 messages at a sample rate of 25 MS/s. We use this data to train a fingerprinting model consisting of an autoencoder combined with a Siamese neural network, enabling the model to learn an efficient encoding of the message headers that preserves identifying information.We demonstrate the fingerprinting system's robustness under attack by replaying messages using a Software-Defined Radio, achieving an Equal Error Rate of 0.120, and ROC AUC of 0.946. Finally, we analyze its stability over time by introducing a time gap between training and testing data, and its extensibility by introducing new transmitters which have not been seen before. We conclude that our techniques are useful for building fingerprinting systems that are stable over time, can be used immediately with new transmitters without retraining, and provide robustness against spoofing and replay attacks by raising the required budget for attacks.

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Section: Conv1dtransposementioning

confidence: 99%

Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting

Smailes,

Köhler,

Birnbach

et al. 2023

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

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“…These features are the intrinsic physical layer features of the device and are only related to the hardware of the device itself 2 . In recent years, RF fingerprinting has been continuously improved in the field of radiation source identification 3 , and significant progress has been made in related theories and practical applications 4 .…”

Section: Introductionmentioning

confidence: 99%

Research on LED fingerprint identification method based on convolutional neural network

Li,

Zhang,

Zhou

et al. 2024

Advanced Fiber Laser Conference (AFL2023)

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Similar to radio frequency fingerprints, the light emitted by light-emitting diode (LED) contains subtle features of the device itself. These subtle features are only related to the physical layer characteristics of the device itself and are intrinsic features of LED, which are difficult to forge and can be used for LED identification. LED fingerprint identification is of great significance in the fields of visible light indoor positioning and secure access to visible light 5G communication networks. This paper experimentally demonstrated LED light fingerprint identification on three commercial LED lamps of the same model and the same batch. To simulate practical application scenarios, this paper conducted 25 spectral measurements on each LED lamp under indoor ambient light noise conditions. Before each spectral measurement, the relative distance and angle between the LED and the spectrometer were randomly adjusted. In response to the one-dimensional data structure of the measured spectrum, this paper replaces the original twodimensional convolutional kernel with a one-dimensional convolutional kernel based on the classic VGG19 network, and adds an adaptive average pooling layer in front of the fully connected layer, proposing an improved VGG19-1D identification algorithm. The improved algorithm was trained from scratch in the training dataset, and after 1200 epochs of training, the test identification accuracy of commercial LED lamps reached 91.30%. The experimental results demonstrated that reliable identification can be achieved based on emitted light spectrum for commercial LED lamps of the same model and the same batch. The improved algorithm has significantly higher identification accuracy than traditional machine learning methods such as support vector machine and random forests.

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“…Until now, many RFF methods have been explored in the literature [7,8]. In order to evaluate these methods, various wireless devices have been used, e.g., Wi-Fi [9], Bluetooth [10][11][12][13][14], RFID [15], and internet-of-things (IoT) transmitters [16,17]. Moreover, RFF methods have also been used for aircraft identification based on ADS-B signals in several studies [18][19][20][21][22][23][24], where the efficiency of RFF in boosting the security of ADS-B systems has been proven.…”

Section: Introductionmentioning

confidence: 99%

A Radio Frequency Fingerprinting-Based Aircraft Identification Method Using ADS-B Transmissions

Gurer,

Dalveren,

Kara

et al. 2024

Aerospace

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The automatic dependent surveillance broadcast (ADS-B) system is one of the key components of the next generation air transportation system (NextGen). ADS-B messages are transmitted in unencrypted plain text. This, however, causes significant security vulnerabilities, leaving the system open to various types of wireless attacks. In particular, the attacks can be intensified by simple hardware, like a software-defined radio (SDR). In order to provide high security against such attacks, radio frequency fingerprinting (RFF) approaches offer reasonable solutions. In this study, an RFF method is proposed for aircraft identification based on ADS-B transmissions. Initially, 3480 ADS-B samples were collected by an SDR from eight aircrafts. The power spectral density (PSD) features were then extracted from the filtered and normalized samples. Furthermore, the support vector machine (SVM) with three kernels (linear, polynomial, and radial basis function) was used to identify the aircraft. Moreover, the classification accuracy was demonstrated via varying channel signal-to-noise ratio (SNR) levels (10–30 dB). With a minimum accuracy of 92% achieved at lower SNR levels (10 dB), the proposed method based on SVM with a polynomial kernel offers an acceptable performance. The promising performance achieved with even a small dataset also suggests that the proposed method is implementable in real-world applications.

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RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context

Cited by 4 publications

References 41 publications

Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting

Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting

Research on LED fingerprint identification method based on convolutional neural network

A Radio Frequency Fingerprinting-Based Aircraft Identification Method Using ADS-B Transmissions

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