A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

Autor: Ignacio Sanchez, Tim Whitworth, Andy McKeown, Gianluca Caparra, Elena Simona Lohan, Wenbo Wang
Přispěvatelé: Tampere University, Electrical Engineering
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Spoofing attack
Computer science
Real-time computing
Context (language use)
02 engineering and technology
TP1-1185
spoofing
Biochemistry
Article
Analytical Chemistry
0202 electrical engineering
electronic engineering
information engineering
Information system
Instrumentation (computer programming)
Electrical and Electronic Engineering
Instrumentation
Chemical technology
213 Electronic
automation and communications engineering
electronics
020208 electrical & electronic engineering
Transmitter
020206 networking & telecommunications
classifiers
radio frequency fingerprinting (RFF)
Atomic and Molecular Physics
and Optics
Feature (computer vision)
GNSS applications
support vector machines (SVM)
global navigation satellite systems (GNSS)
Noise (video)
feature extractors
I/Q (pre-correlation) data
Zdroj: Sensors
Volume 21
Issue 9
Sensors, Vol 21, Iss 3012, p 3012 (2021)
Sensors (Basel, Switzerland)
ISSN: 1424-8220
DOI: 10.3390/s21093012
Popis: Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context
and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.
Databáze: OpenAIRE
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