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In this study, a novel method is proposed to detect and classify the threats for fiber optic distributed acoustic sensing (DAS) systems. In the study, phase-sensitive optical time-domain reflectometry (phase-OTDR) is realized for the sensing system. The proposed method is consisted of three main stages. In the first stage, Wavelet denoising method is applied for noise reduction in the measured signal, and difference in time domain approach is used to perform high-pass filtering. Autocorrelation is then used for comparing the signal with itself over time in each bin to remove uncorrelated signals. Next, the power of the correlated signals at each bin is calculated and sorted where maximum valued bins are considered as the event signal. In the second stage, Variational Mode Decomposition (VMD) technique is used to decompose the detected event signals into a series of band-limited modes from which the event signals are reconstructed. From the reconstructed event signals, higher order statistical (HOS) features including variance, skewness, and kurtosis are extracted. In the last stage, the threats are discriminated by implementing Linear Support Vector Machine (LSVM)-based classification approach to the extracted features. In order to evaluate the effects of proposed method on the classification performance, different types of activities such as digging with hammer, pickaxe, and shovel collected from various points of a buried fiber optic cable have been used under different Signal-to-Noise Ratio (SNR) levels (-4 to -18 dB). It has observed that the classification accuracy at high/moderate (-4 to -8 dB) and low (-8 to -18 dB) SNR levels are 79.5% and 75.2%, respectively. To the best of authors' knowledge, this research study is the first report to use VMD technique for threat classification in phase-OTDR-based DAS systems. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Radio frequency fingerprinting (RFF) is based on identification of unique features of RF transient signals emitted by radio devices. RF transient signals of radio devices are short in duration, non-stationary and nonlinear time series. This paper evaluates the performance of RF fingerprinting method based on variational mode decomposition (VMD). For this purpose, VMD is used to decompose Bluetooth (BT) transient signals into a series of band-limited modes, and then, the transient signal is reconstructed from the modes. Higher order statistical (HOS) features are extracted from the complex form of reconstructed transients. Then, Linear Support Vector Machine (LVM) classifier is used to identify BT devices. The method has been tested experimentally with BT devices of different brands, models and series. The classification performance shows that VMD based RF fingerprinting method achieves better performance (at least 8% higher) than time-frequency-energy (TFED) distribution based methods such as Hilbert-Huang Transform. This is demonstrated with the same dataset but with smaller number of features (nine features) and slightly lower (2-3 dB) SNR levels.

Radio frequency fingerprinting (RFF) is one of the communication network&rsquo, s security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (~4% higher) at lower SNR levels (&minus, 5&ndash, 5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.