Title: GNSS Receiver Fingerprinting for Security-Enhanced Applications
Author(s): Daniele Borio, Ciro Gioia, Gianmarco Baldini, Joaquin Fortuny
Published in: Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016)
September 12 - 16, 2016
Oregon Convention Center
Portland, Oregon
Pages: 2960 - 2970
Cite this article: Borio, Daniele, Gioia, Ciro, Baldini, Gianmarco, Fortuny, Joaquin, "GNSS Receiver Fingerprinting for Security-Enhanced Applications," Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016), Portland, Oregon, September 2016, pp. 2960-2970.
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Abstract: Global Navigation Satellite System (GNSS) data faking is similar to node forgery in a wireless network: a simulator or another device can be used to impersonate an actual GNSS receiver in a system which uses GNSS services. In this way, misleading Position, Velocity and Time (PVT) information can be send to the final PVT user in the system. To mitigate the risk of GNSS data faking, GNSS receiver fingerprinting can be adopted in security-enhanced applications to verify, at least to a certain extent, the authenticity of GNSS data. For example, the injection of GNSS fake data in an Intelligent Transport System (ITS) vehicle platform could be identified using GNSS receiver fingerprinting. This paper investigates the potential of receiver clock bias and drift as sources of features for fingerprinting. In particular, several features, including Allan Deviation (ADEV), maximum and Root Mean Square (RMS) Time Interval Error (TIE) and correlation of the clock time series, have been investigated. The potential of the different features has been empirically investigated. It shows that three features are sufficient to discriminate the different receiver types. In particular, the ADEV and the Maximum TIE (MTIE) at 1 second, and the correlation value at 20 seconds have been selected for fingerprinting. These features allow one to effectively cluster the different receiver types and to build a “white list” for receiver identification.