Machine and Representation Learning-Based GNSS Spoofing Detectors Utilizing Feature Set From Generic GNSS Receivers
Asif Iqbal, Muhammad Naveed Aman, Biplab Sikdar
Abstract
The Global Navigation Satellite System (GNSS) plays critical role in providing precise timing and positioning information for various applications. However, its civilian signals are vulnerable to spoofing attacks, necessitating robust detection methods. While supervised learning has shown promise in spoof detection, it requires distinct features in the training data for effective learning. In this study, we propose a novel training feature set that combines power and Signal Quality Monitoring (SQM) metrics from a single-antenna GNSS receiver. We introduce a Two-Stage Artificial Neural Network (TS-ANN) that leverages this feature set alongside multi-correlator finger values for effective spoof detection. Furthermore, supervised learning models often struggle with previously unseen attacks due to limited overlap with the training data. To address this, we present a zero-day attack detector based on unsupervised representation learning using a Variational Autoencoder (VAE) which is trained exclusively on authentic datasets, enhancing its ability to detect novel attack patterns. Experimental results on publicly available TEXBAT datasets demonstrate that our TS-ANN achieves a detection probability (PD) exceeding 99% for similar test datasets. In more sophisticated attack scenarios, such as DS-7, performance may decrease to 50.68%. Nevertheless, our zero-day detector maintains a PD exceeding 92.5%, highlighting its resilience to previously unseen attacks.