Next Article in Journal
Optical Thickness Monitoring as a Strategic Element for the Development of SPR Sensing Applications
Previous Article in Journal
A Study on the Characteristics of the Ionospheric Gradient under Geomagnetic Perturbations
Previous Article in Special Issue
Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I
Open AccessArticle

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

1
Department of Communication, Information, Systems & Sensors, Royal Military Academy, 1000 Brussels, Belgium
2
Department of Industrial Systems Engineering and Product Design, Ghent University, 9052 Ghent, Belgium
3
Industrial Systems Engineering (ISyE), Flanders Make, Ghent University, 9052 Ghent, Belgium
4
Septentrio N.V., 3000 Leuven, Belgium
*
Author to whom correspondence should be addressed.
This paper is a Part II of the 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(7), 1806; https://doi.org/10.3390/s20071806 (registering DOI)
Received: 25 February 2020 / Revised: 19 March 2020 / Accepted: 23 March 2020 / Published: 25 March 2020
Global Navigation Satellite System (GNSS) meaconing and spoofing are being considered as the key threats to the Safety-of-Life (SoL) applications that mostly rely upon the use of open service (OS) signals without signal or data-level protection. While a number of pre and post correlation techniques have been proposed so far, possible utilization of the supervised machine learning algorithms to detect GNSS meaconing and spoofing is currently being examined. One of the supervised machine learning algorithms, the Support Vector Machine classification (C-SVM), is proposed for utilization at the GNSS receiver level due to fact that at that stage of signal processing, a number of measurements and observables exists. It is possible to establish the correlation pattern among those GNSS measurements and observables and monitor it with use of the C-SVM classification, the results of which we present in this paper. By adding the real-world spoofing and meaconing datasets to the laboratory-generated spoofing datasets at the training stage of the C-SVM, we complement the experiments and results obtained in Part I of this paper, where the training was conducted solely with the use of laboratory-generated spoofing datasets. In two experiments presented in this paper, the C-SVM algorithm was cross-fed with the real-world meaconing and spoofing datasets, such that the meaconing addition to the training was validated by the spoofing dataset, and vice versa. The comparative analysis of all four experiments presented in this paper shows promising results in two aspects: (i) the added value of the training dataset enrichment seems to be relevant for real-world GNSS signal manipulation attempt detection and (ii) the C-SVM-based approach seems to be promising for GNSS signal manipulation attempt detection, as well as in the context of potential federated learning applications. View Full-Text
Keywords: global navigation satellite system; spoofing; meaconing; support vector machines; principal component analysis; model validation; safety-of-life; position-navigation-timing; federated learning; GPS; GNSS; PNT; SVM; SoL global navigation satellite system; spoofing; meaconing; support vector machines; principal component analysis; model validation; safety-of-life; position-navigation-timing; federated learning; GPS; GNSS; PNT; SVM; SoL
Show Figures

Figure 1

MDPI and ACS Style

Semanjski, S.; Semanjski, I.; De Wilde, W.; Gautama, S. Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part II. Sensors 2020, 20, 1806.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop