2020
DOI: 10.3390/s20041171
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Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I

Abstract: The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user’s system being aware, resulting in presented hazardous misleading information and signal integrity deterioration without an alarm being triggered. Among the number of proposed spoofing detection and mitigation techniques appl… Show more

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Cited by 44 publications
(31 citation statements)
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“…The correlation patterns within those data can be established and monitored with the purpose of distinguishing between false and authentic GNSS measurements and observables. In addition, the application of an SVM classifier to GNSS spoofing detection has been previously demonstrated as promising by [8][9][10][11].…”
Section: Introductionmentioning
confidence: 99%
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“…The correlation patterns within those data can be established and monitored with the purpose of distinguishing between false and authentic GNSS measurements and observables. In addition, the application of an SVM classifier to GNSS spoofing detection has been previously demonstrated as promising by [8][9][10][11].…”
Section: Introductionmentioning
confidence: 99%
“…As was described in depth in Part I of this publication [11], various datasets for the C-Support Vector Machine model building and its validation have been used. As in Experiments I and II [11], all three groups of GNSS datasets are also used in Experiments III and IV.…”
Section: Introductionmentioning
confidence: 99%
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