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Open AccessArticle

Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I

1
Department of Communication, Information, Systems & Sensors, Royal Military Academy, 1000 Brussels, Belgium
2
Department of Industrial Systems Engineering and Product Design, Ghent University, 9000 Ghent, Belgium
3
Industrial Systems Engineering (ISyE), Flanders Make, Ghent University, 9000 Ghent, Belgium
4
Septentrio N.V., 3001 Leuven, Belgium
*
Author to whom correspondence should be addressed.
This paper is an extended version of the paper “Use and Validation of Supervised Machine Learning Approach for Detection of GNSS Signal Spoofing” presented at the 9th International Conference on Localization and GNSS (ICL-GNSS 2019).
Sensors 2020, 20(4), 1171; https://doi.org/10.3390/s20041171
Received: 25 January 2020 / Revised: 18 February 2020 / Accepted: 19 February 2020 / Published: 20 February 2020
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 applied at different stages of the signal processing, we present a method for the cross-correlation monitoring of multiple and statistically significant GNSS observables and measurements that serve as an input for the supervised machine learning detection of potentially spoofed or meaconed GNSS signals. The results of two experiments are presented, in which laboratory-generated spoofing signals are used for training and verification within itself, while two different real-world spoofing and meaconing datasets were used for the validation of the supervised machine learning algorithms for the detection of the GNSS spoofing and meaconing. View Full-Text
Keywords: global navigation satellite system; spoofing; meaconing; support vector machines; principal component analysis; safety-of-life; position-navigation-timing; GPS; classification global navigation satellite system; spoofing; meaconing; support vector machines; principal component analysis; safety-of-life; position-navigation-timing; GPS; classification
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Semanjski, S.; Semanjski, I.; De Wilde, W.; Muls, A. Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I. Sensors 2020, 20, 1171.

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