A Radio Frequency Fingerprinting-Based Aircraft Identification Method Using Ads-B Transmissions

dc.authorid Kara, Ali/0000-0002-9739-7619
dc.authorscopusid 58956429200
dc.authorscopusid 51763497600
dc.authorscopusid 7102824862
dc.authorscopusid 35408917600
dc.contributor.author Gurer, Gursu
dc.contributor.author Dalveren, Yaser
dc.contributor.author Kara, Ali
dc.contributor.author Derawi, Mohammad
dc.contributor.other Department of Electrical & Electronics Engineering
dc.date.accessioned 2024-07-05T15:23:08Z
dc.date.available 2024-07-05T15:23:08Z
dc.date.issued 2024
dc.department Atılım University en_US
dc.department-temp [Gurer, Gursu; Kara, Ali] Gazi Univ, Dept Elect & Elect Engn, TR-06570 Ankara, Turkiye; [Dalveren, Yaser] Atilim Univ, Dept Elect & Elect Engn, TR-06830 Ankara, Turkiye; [Derawi, Mohammad] Norwegian Univ Sci & Technol, Dept Elect Syst, N-2815 Gjovik, Norway en_US
dc.description Kara, Ali/0000-0002-9739-7619 en_US
dc.description.abstract The automatic dependent surveillance broadcast (ADS-B) system is one of the key components of the next generation air transportation system (NextGen). ADS-B messages are transmitted in unencrypted plain text. This, however, causes significant security vulnerabilities, leaving the system open to various types of wireless attacks. In particular, the attacks can be intensified by simple hardware, like a software-defined radio (SDR). In order to provide high security against such attacks, radio frequency fingerprinting (RFF) approaches offer reasonable solutions. In this study, an RFF method is proposed for aircraft identification based on ADS-B transmissions. Initially, 3480 ADS-B samples were collected by an SDR from eight aircrafts. The power spectral density (PSD) features were then extracted from the filtered and normalized samples. Furthermore, the support vector machine (SVM) with three kernels (linear, polynomial, and radial basis function) was used to identify the aircraft. Moreover, the classification accuracy was demonstrated via varying channel signal-to-noise ratio (SNR) levels (10-30 dB). With a minimum accuracy of 92% achieved at lower SNR levels (10 dB), the proposed method based on SVM with a polynomial kernel offers an acceptable performance. The promising performance achieved with even a small dataset also suggests that the proposed method is implementable in real-world applications. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.3390/aerospace11030235
dc.identifier.issn 2226-4310
dc.identifier.issue 3 en_US
dc.identifier.scopus 2-s2.0-85188677430
dc.identifier.uri https://doi.org/10.3390/aerospace11030235
dc.identifier.uri https://hdl.handle.net/20.500.14411/2258
dc.identifier.volume 11 en_US
dc.identifier.wos WOS:001191761100001
dc.institutionauthor Dalveren, Yaser
dc.institutionauthor Kara, Ali
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 2
dc.subject automatic dependent surveillance-broadcast en_US
dc.subject deep learning en_US
dc.subject radio frequency fingerprinting en_US
dc.subject wireless security en_US
dc.title A Radio Frequency Fingerprinting-Based Aircraft Identification Method Using Ads-B Transmissions en_US
dc.type Article en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication
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