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

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.

Convolutional Neural Network for Multipath Detection in GNSS Receivers

“…Deep learning models in GNSS systems were also used for the problem of spoofing attack detection in GNSS receiver [25]. In this work, early-late phase, delta and signal level are the three main features extracted from the correlation output of the tracking loop.…”

SCER Spoofing Attacks on the Galileo Open Service and Machine Learning Techniques for End-User Protection

“…Note that equations (17), (18), (21) and (22) are considering that the Spoofer is only using E1B PRN in the local copy in the receiver used to estimate the unpredictable symbol. For more details on this, please refer to Sections II-D1 or II-D2.…”

“…Based on these results (equations: (9), (10), (21) and (22)), assuming both symbols transmitted by the satellite have the same probability, then:…”

Adaptive Spoofing Suppression Algorithm for GNSS Based on Multiple Antennas Array

“…When an antenna is rotating, the power measurements of the spoofing signals coming from the same direction change similarly and the correlation coefficients between them are close to 1, but the power measurements of the authentic signals are uncorrelated [15]. A new approach for GPS spoofing detection based on a multi-layer neural network (NN) whose inputs are indices of features is presented in [16]. This method demonstrated adequate detection accuracy from the NN with a short detection time.…”