Detection of Spoofing Attack Based on Multi-Layer Neural Network in Single-Frequency GPS Receivers

Authors

1 Master's student, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

2 Professor, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

3 PhD student, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

A GPS spoofing attack attempts to deceive a GPS receiver by broadcasting counterfeit GPS signals. Structured to
resemble a set of normal GPS signals, but it is a little stronger. In the recent years, there have been presented many
different solutions for detection and reduction of spoofing attack. Neural Networks (NNs) are the modern
computational method for learning machine and then imposing the acquired knowledge for predicting the output response
of complicated systems. This paper presents a main approach to GPS spoofing detection based on intelligent
systems. Signals are classified using auto-correlation features. Indices of early-late phase, delta and total signal level
as inputs of multi-layer NN in order to detect spoofing signal in GPS receiver tracking loop. Authentic and spoof
signals have different statistical pattern in named parameter and NN can detected it. Since NN is able to exploit
multiple features from different methods, it classifies signals with error less than the conventional techniques. Finally,
the least precision obtained from simulation of NN based GPS software receiver is 98.78% in correct detection of
spoofing signal from valid signal. Moreover, the detection time is less than the existing methods.

Keywords

References

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Electronic and Cyber Defense
Volume 3, Issue 1 - Serial Number 1
November 2020
Pages 69-80

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History

  • Receive Date: 25 December 2014
  • Revise Date: 21 June 2023
  • Accept Date: 19 September 2018
  • Publish Date: 21 April 2015

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