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Article Citation - WoS: 9Citation - Scopus: 12On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection(Mdpi, 2022) Mohamed, Ismail; Dalveren, Yaser; Catak, Ferhat Ozgur; Kara, AliIn the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC- a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set ofWi-Fi signals captured from variousWi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signalto-noise ratio (SNR) values defined as low (3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.Article Citation - WoS: 11Citation - Scopus: 15A Study on the Performance Evaluation of Wavelet Decomposition in Transient-Based Radio Frequency Fingerprinting of Bluetooth Devices(Wiley, 2022) Almashaqbeh, Hemam; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) is used as a physical-layer security method to provide security in wireless networks. Basically, it exploits the distinctive features (fingerprints) extracted from the physical waveforms emitted from radio devices in the network. One of the major challenges in RFF is to create robust features forming the fingerprints of radio devices. Here, dual-tree complex wavelet transform (DT-CWT) provides an accurate way of extracting those robust features. However, its performance on the RFF of Bluetooth transients which fall into narrowband signaling has not been reported yet. Therefore, this study examines the performance of DT-CWT features on the use of transient-based RFF of Bluetooth devices. Initially, experimentally collected Bluetooth transients from different smartphones are decomposed by DT-CWT. Then, the characteristics and statistics of the wavelet domain signal are exploited to create robust features. Next, the support vector machine (SVM) is used to classify the smartphones. The classification accuracy is demonstrated by varying channel signal-to-noise ratio (SNR) and the size of transient duration. Results show that reasonable accuracy can be achieved (lower bound of 88%) even with short transient duration (1024 samples) at low SNRs (0-5 dB).Article Citation - WoS: 29Citation - Scopus: 33On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices(Mdpi, 2020) Aghnaiya, Alghannai; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) is one of the communication network'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 (similar to 4% higher) at lower SNR levels (-5-5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.Article Citation - WoS: 11Citation - Scopus: 14Performance Assessment of Transient Signal Detection Methods and Superiority of Energy Criterion (ec) Method(Ieee-inst Electrical Electronics Engineers inc, 2020) Mohamed, Ismail S.; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) based on RF transients is one of the most effective techniques for improving wireless security. For an efficient RFF development, RF transients need to be accurately detected. However, the detection of the transient starting point remains a main challenge due to the channel noise. In the literature, several methods have been presented to detect the starting point of the transient signals. As an alternative to these methods, this study proposes a method that utilizes Energy Criterion (EC) technique for the first time. In order to test its performance, firstly, an extensive dataset consisting of Wi-Fi signals recorded under realistic Signal-to-Noise Ratio (SNR) conditions is created. Using the provided dataset, the proposed method as well as common transient detection methods are employed for transient start detection. Then, the effect of SNR on the performance of transient start detection is evaluated. Moreover, a performance comparison between the methods is provided based on their respective computational speed and complexity. The results prove the feasibility and efficiency of the proposed method to detect the transient starting point for RFF of Wi-Fi device identification. As to the knowledge of the authors, this study is the first report that comparatively assesses the transient detection methods by using extensive data under realistic noise conditions.
