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Author: Catak, Ferhat OzgurScopus Q: Q2

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    Article
    Citation - WoS: 9
    Citation - Scopus: 12
    On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection
    (Mdpi, 2022) Mohamed, Ismail; Dalveren, Yaser; Catak, Ferhat Ozgur; Kara, Ali
    In 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.
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    Citation - WoS: 5
    Citation - Scopus: 6
    Deployment and Implementation Aspects of Radio Frequency Fingerprinting in Cybersecurity of Smart Grids
    (Mdpi, 2023) Awan, Maaz Ali; Dalveren, Yaser; Catak, Ferhat Ozgur; Kara, Ali
    Smart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart grids, cybersecurity of IoT is paramount. To this end, use of cryptographic security methods is prevalent in existing IoT. Non-cryptographic methods such as radio frequency fingerprinting (RFF) have been on the horizon for a few decades but are limited to academic research or military interest. RFF is a physical layer security feature that leverages hardware impairments in radios of IoT devices for classification and rogue device detection. The article discusses the potential of RFF in wireless communication of IoT devices to augment the cybersecurity of smart grids. The characteristics of a deep learning (DL)-aided RFF system are presented. Subsequently, a deployment framework of RFF for smart grids is presented with implementation and regulatory aspects. The article culminates with a discussion of existing challenges and potential research directions for maturation of RFF.