{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:48:16Z","timestamp":1780318096577,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20041171","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T10:49:16Z","timestamp":1582282156000},"page":"1171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data\u2014Part I"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9226-4112","authenticated-orcid":false,"given":"Silvio","family":"Semanjski","sequence":"first","affiliation":[{"name":"Department of Communication, Information, Systems &amp; Sensors, Royal Military Academy, 1000 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2795-8094","authenticated-orcid":false,"given":"Ivana","family":"Semanjski","sequence":"additional","affiliation":[{"name":"Department of Industrial Systems Engineering and Product Design, Ghent University, 9000 Ghent, Belgium"},{"name":"Industrial Systems Engineering (ISyE), Flanders Make, Ghent University, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wim","family":"De Wilde","sequence":"additional","affiliation":[{"name":"Septentrio N.V., 3001 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alain","family":"Muls","sequence":"additional","affiliation":[{"name":"Department of Communication, Information, Systems &amp; Sensors, Royal Military Academy, 1000 Brussels, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2897166","article-title":"A Survey and Analysis of the GNSS Spoofing Threat and Countermeasures","volume":"48","author":"Schmidt","year":"2016","journal-title":"ACM Comput. 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