Browsing by Author "Ali, Aysha M."

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Citation - WoS: 52
Citation - Scopus: 68
Assessment of Features and Classifiers for Bluetooth Rf Fingerprinting
(Ieee-inst Electrical Electronics Engineers inc, 2019) Ali, Aysha M.; Uzundurukan, Emre; Kara, Ali; Department of Electrical & Electronics Engineering; Airframe and Powerplant Maintenance; 15. Graduate School of Natural and Applied Sciences; 13. School of Civil Aviation (4-Year); 01. Atılım University
Recently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.
Design of Low-Cost Modular Rf Front End for Rf Fingerprinting of Bluetooth Signals
(Ieee, 2017) Uzundurukan, Emre; Ali, Aysha M.; Kara, Ali; Department of Electrical & Electronics Engineering; Airframe and Powerplant Maintenance; 15. Graduate School of Natural and Applied Sciences; 13. School of Civil Aviation (4-Year); 01. Atılım University
For RF fingerprinting of wireless devices, data acquisition has a critical role. Because of this, highly sophisticated devices arc used for data capturing or acquisition. In this paper, design of a RF receiver front end with modular components is presented. This design contains filtering and down conversion processing of Bluetooth signals for cellular phones. Moreover, AWR VSS and MATLAB have been used for simulating the down converter circuit. With this simulation, effects of components that used in the design on recorded signal have been observed. In this work in progress paper, only high SNR conditions are considered.
Improvements on Transient Signal Detection for Rf Fingerprinting
(Ieee, 2017) Ali, Aysha M.; Uzundurukan, Emre; Kara, Ali; Department of Electrical & Electronics Engineering; Airframe and Powerplant Maintenance; 15. Graduate School of Natural and Applied Sciences; 13. School of Civil Aviation (4-Year); 01. Atılım University
The detection of the transient signal plays a significant role in RF fingerprinting for transmitter devices. This paper proposes modifications on two transient signal detection methods, namely, Bayesian change point detection and phase based detection. For initial tests, Bluetooth signals captured in the laboratory are used for performance analysis of the proposed methods. Preliminary results show that the proposed methods can be used in RF fingerprinting of wireless devices.
Citation - WoS: 16
Citation - Scopus: 19
Performance Analysis of Modular Rf Front End for Rf Fingerprinting of Bluetooth Devices
(Springer, 2020) Uzundurukan, Emre; Ali, Aysha M.; Dalveren, Yaser; Kara, Ali; Department of Electrical & Electronics Engineering; Airframe and Powerplant Maintenance; 15. Graduate School of Natural and Applied Sciences; 13. School of Civil Aviation (4-Year); 01. Atılım University
Radio frequency fingerprinting (RFF) could provide an efficient solution to address the security issues in wireless networks. The data acquisition system constitutes an important part of RFF. In this context, this paper presents an implementation of a modular RF front end system to be used in data acquisition for RFF. Modularity of the system provides flexible implementation options to suit diverse frequency bands with different applications. Moreover, the system is able to collect data by means of any digitizer, and enable to record the data at lower frequencies. Therefore, proposed RF front end system becomes a low-cost alternative to existing devices used in data acquisition. In its implementation, Bluetooth (BT) signals were used. Initially, transients of BT signals were detected by utilizing a large number of BT devices (smartphones). From the detected transients, distinctive signal features were extracted. Then, support vector machine (SVM) and neural networks (NN) classifiers were implemented to the extracted features for evaluating the feasibility of proposed system in RFF. As a result, 96.9% and 96.5% classification accuracies on BT devices have been demonstrated for SVM and NN classifiers respectively.
Citation - WoS: 30
Citation - Scopus: 39
Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices
(Ieee-inst Electrical Electronics Engineers inc, 2019) Aghnaiya, Alghannai; Ali, Aysha M.; Kara, Ali; Department of Electrical & Electronics Engineering; 15. Graduate School of Natural and Applied Sciences; 01. Atılım University
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.