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master's thesis
Detekcija lažnog signala u sustavu GNSS
2022. urn:nbn:hr:179:990249
Brkić, Marta
University of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Department of Electronics and Computing

Cite this document

Brkić, M. (2022). Detekcija lažnog signala u sustavu GNSS (Master's thesis). Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:179:990249

Brkić, Marta. "Detekcija lažnog signala u sustavu GNSS." Master's thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:179:990249

Brkić, Marta. "Detekcija lažnog signala u sustavu GNSS." Master's thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2022. https://urn.nsk.hr/urn:nbn:hr:179:990249

Brkić, M. (2022). 'Detekcija lažnog signala u sustavu GNSS', Master's thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, accessed 14 November 2024, https://urn.nsk.hr/urn:nbn:hr:179:990249

Brkić M. Detekcija lažnog signala u sustavu GNSS [Master's thesis]. Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture; 2022 [cited 2024 November 14] Available at: https://urn.nsk.hr/urn:nbn:hr:179:990249

M. Brkić, "Detekcija lažnog signala u sustavu GNSS", Master's thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:179:990249

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Metadata
TitleDetekcija lažnog signala u sustavu GNSS
AuthorMarta Brkić
MentorDinko Begušić (mentor)
GranterUniversity of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
(Department of Electronics and Computing)
Split
Defense date and country2022-09-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Sa sve širom upotrebom sustava za navigaciju i pozicioniranje te s pojavom jeftinih softverski definiranih radija, porastao je broj napada lažiranjem lokacije. Kako bi se ovakvi napadi na vrijeme detektirali i izbjegli, potrebno je raditi na poboljšavanju sustava za detekciju napada lažiranjem i za cilj imati suzbijanje ovakvih napada u što je moguće većoj mjeri. Metode mogu biti različite hardverske ili softverske, pa i metode strojnog učenja. U ovom radu opisan je sustav GNSS te njegove značajke i protokol kojeg koristi za komunikaciju. Detaljnije je opisan napad lažiranjem te su opisane različite metode za detekciju lažnog signala. Praktični dio rada obuhvaća razvoj laboratorijskog modela za izvođenje napada lažiranjem uz primjenu programskog okruženja GNSS-SDR pomoću kojeg se prikupljaju podaci o lokaciji. Primjenom aplikacije Classification Learner proveden je postupak klasifikacije GNSS signala sa svrhom prepoznavanja lažnog signala. Prikazano je kako različiti modeli strojnog učenja s visokom točnošću klasificiraju signale u klase stvarni i lažni signali. Visoka točnost istreniranih modela potvrđuje zaključak kako su ove metode prikladan i pouzdan pristup za detekciju lažnog signala u sustavu GNSS.
Abstract (english)
The number of spoofing attacks is more noticeable with the increasing use of navigation and positioning systems and with the apperance of inexpensive software-defined radios. In order to detect and avoid such attacks at the right time, it is necessary to work on improving systems for detecting these spoofing attacks and suppress such attacks as much as possible. There are many different spoofing detection methods which can be hardware or software even machine learning methods. The GNSS system with features and the protocol which it uses for communication are described in this paper. The spoofing attack is described in more detail, as well as various methods for spoofing detection. The practical part of the work includes development of a laboratory model for performing spoofing attack with the application GNSS-SDR which is used to collect location data. Using Classification Learner application, the GNSS signal classification was carried out with the purpose of recognizing spoofed signal. It is shown how different machine learning models classify signals into classes real and spoofed with high accuracy. The high accuracy of the trained models confirms the conclusion that these methods are suitable and reliable approach for spoofed signal detection in the GNSS system.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:179:990249
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to authenticated users
Terms of useLicence In copyright icon
Created on2023-03-09 10:56:49