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Detection of Spoofing Attack using Machine Learning based on Multi-Layer Neural Network in Single-Frequency GPS Receivers

Published online by Cambridge University Press:  14 August 2017

E. Shafiee ,
M. R. Mosavi  and
M. Moazedi
E. Shafiee
Affiliation:
(Department of Electrical Engineering, Iran University of Science and Technology Narmak, Tehran 16846-13114, Iran)
M. R. Mosavi*
Affiliation:
(Department of Electrical Engineering, Iran University of Science and Technology Narmak, Tehran 16846-13114, Iran)
M. Moazedi
Affiliation:
(Department of Electrical Engineering, Iran University of Science and Technology Narmak, Tehran 16846-13114, Iran)
*
(E-mail: m_mosavi@iust.ac.ir)
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Abstract

The importance of the Global Positioning System (GPS) and related electronic systems continues to increase in a range of environmental, engineering and navigation applications. However, civilian GPS signals are vulnerable to Radio Frequency (RF) interference. Spoofing is an intentional intervention that aims to force a GPS receiver to acquire and track invalid navigation data. Analysis of spoofing and authentic signal patterns represents the differences as phase, energy and imaginary components of the signal. In this paper, early-late phase, delta, and signal level as the three main features are extracted from the correlation output of the tracking loop. Using these features, spoofing detection can be performed by exploiting conventional machine learning algorithms such as K-Nearest Neighbourhood (KNN) and naive Bayesian classifier. A Neural Network (NN) as a learning machine is a modern computational method for collecting the required knowledge and predicting the output values in complicated systems. This paper presents a new approach for GPS spoofing detection based on multi-layer NN whose inputs are indices of features. Simulation results on a software GPS receiver showed adequate detection accuracy was obtained from NN with a short detection time.

Keywords

GPS vulnerabilitySpoofing attackSpoofing detectionFeaturesNeural network
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 1 , January 2018 , pp. 169 - 188
Copyright
Copyright © The Royal Institute of Navigation 2017 

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