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  4. Deep Learning Detection of GPS Spoofing
 
conference paper

Deep Learning Detection of GPS Spoofing

Jullian, Olivia
•
Otero, Beatriz
•
Stojilović, Mirjana  
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February 2, 2022
Machine Learning, Optimization, and Data Science
7th International Conference Machine Learning, Optimization, and Data Science (LOD 2021)

Unmanned aerial vehicles (UAVs) are widely deployed in air navigation, where numerous applications use them for safety-of-life and positioning, navigation, and timing tasks. Consequently, GPS spoofing attacks are more and more frequent. The aim of this work is to enhance GPS systems of UAVs, by providing the ability of detecting and preventing spoofing attacks. The proposed solution is based on a multilayer perceptron neural network, which processes the flight parameters and the GPS signals to generate alarms signaling GPS spoofing attacks. The obtained accuracy lies between 83.23% for TEXBAT dataset and 99.93% for MAVLINK dataset.

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Type
conference paper
DOI
10.1007/978-3-030-95467-3_38
Author(s)
Jullian, Olivia
Otero, Beatriz
Stojilović, Mirjana  
Costa, Juan José
Verdú, Javier
Pajuelo, Manuel Alejandro
Date Issued

2022-02-02

Publisher

Springer, Cham

Published in
Machine Learning, Optimization, and Data Science
ISBN of the book

978-3-030954-67-3

Series title/Series vol.

Lecture Notes in Computer Science; 13163

Start page

527

End page

540

Subjects

Deep learning

•

Intrusion detection model

•

Unmanned aerial vehicles

•

Spoofing

•

Global navigation satellite system

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
PARSA  
Event nameEvent placeEvent date
7th International Conference Machine Learning, Optimization, and Data Science (LOD 2021)

Grasmere, UK

October 4-8, 2021

Available on Infoscience
February 8, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/185213
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