Deep Learning Methods for IoT Device Authentication Using Symbols Density Trace Plot

Huang, Da; Al-Hourani, Akram; Sithamparanathan, Kandeepan; Rowe, Wayne S. T.

doi:10.1109/jiot.2024.3361892

Cited by 2 publications

(2 citation statements)

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“…Each burst has a size of 8192 samples. This number of devices is consistent with other similar studies using LoRa devices for RF fingerprints, like [27], where 10 devices were also used, or other RFF studies like [13],where 11 devices were used, and [9], where 5 devices were used.…”

Section: Data Setsupporting

confidence: 89%

“…The literature on radio frequency fingerprinting is quite vast, and many studies have been published in recent years, as described in recent surveys like [3,7]. While initial studies [8] used handcrafted features in combination with ML to implement the classification, the application of DL to RFF was demonstrated to be highly successful in [9][10][11]. For this reason, this study also uses DL but in combination with the application of VMD.…”

Section: Related Workmentioning

confidence: 99%

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LoRa Radio Frequency Fingerprinting with Residual of Variational Mode Decomposition and Hybrid Machine-Learning/Deep-Learning Optimization

Baldini,

Bonavitacola

2024

Electronics

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RFF refers to the technique for identifying and classifying wireless devices on the basis of their physical characteristics, which appear in the digital signal transmitted in space. Small differences in the radio frequency front-end of the wireless devices are generated across the same wireless device model during the implementation and manufacturing process. These differences create small variations in the transmitted signal, even if the wireless device is still compliant with the wireless standard. By using data analysis and machine-learning algorithms, it is possible to classify different electronic devices on the basis of these variations. This technique has been well proven in the literature, but research is continuing to improve the classification performance, robustness to noise, and computing efficiency. Recently, DL has been applied to RFF with considerable success. In particular, the combination of time-frequency representations and CNN has been particularly effective, but this comes at a great computational cost because of the size of the time-frequency representation and the computing time of CNN. This problem is particularly challenging for wireless standards, where the data to be analyzed is extensive (e.g., long preambles) as in the case of the LoRa (Long Range) wireless standard. This paper proposes a novel approach where two pre-processing steps are adopted to (1) improve the classification performance and (2) to decrease the computing time. The steps are based on the application of VMD where (in opposition to the known literature) the residual of the VMD application is used instead of the extracted modes. The concept is to remove the modes, which are common among the LoRa devices, and keep with the residuals the unique intrinsic features, which are related to the fingerprints. Then, the spectrogram is applied to the residual component. Even after this step, the computing complexity of applying CNN to the spectrogram is high. This paper proposes a novel step where only segments of the spectrogram are used as input to CNN. The segments are selected using a machine-learning approach applied to the features extracted from the spectrogram using the LBP. The approach is applied to a recent LoRa radio frequency fingerprinting public data set, where it is shown to significantly outperform the baseline approach based on the full use of the spectrogram of the original signal in terms of both classification performance and computing complexity.

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Section: Data Setsupporting

confidence: 89%