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Blind spoofing detection for multi-antenna snapshot receivers using machine-learning techniques

: Rossouw van der Merwe, J.; Nikolikj, A.; Kram, S.; Lukcin, I.; Nadzinski, G.; Rügamer, A.; Felber, W.


Institute of Navigation -ION-, Manassas/Va.; Institute of Navigation -ION-, Satellite Division, Washington/DC:
33rd International Technical Meeting of the Satellite Division of The Institute of Navigation, ION GNSS+ 2020. Proceedings : September 21 - 25, 2020, Virtual
Fairfax/Va.: ION, 2020
ISBN: 0-936406-26-7
ISBN: 978-0-936406-26-8
Institute of Navigation, Satellite Division (ION GNSS International Technical Meeting) <33, 2020, Online>
Fraunhofer IIS ()

Spoofing, the transmission of false global navigation satellite system (GNSS) signals, is a problem for a GNSS receiver. Therefore, a spoofing attack should be detected by a receiver to ensure the integrity of the position, velocity, and time (PVT) solution. Detecting an attack is more difficult for a snapshot receiver, as temporal changes cannot be used as detection metrics. Further, if the spoofing attacker has access to the receiver, then ideal conditions for spoofing can be facilitated. This paper presents a machine learning (ML) approach of detecting a spoofing attack on a multi-antenna snapshot receiver. Blind detection methods are incorporated, as it is assumed that the antenna array could have been tampered with. The ML approaches include logistic regression (LR), K-nearest neighbors (KNN), na?ve Bayes (NB), decision tree (DT) and support vector machine (SVM) algorithms. To ensure sufficient variance for training of the models, a spoofing simulation platform is developed and described in the paper. Training and testing is done on both simulated and real world data sets. Preliminary results indicate good classification, when training on the simulated data and validating on the real recorded data. Several of the ML methods have a classification f1-score exceeding 99 %. Even simple ML methods, like LR, KNN and NB, show good performance, indicating that the selected features are already adequately separating the spoofing and real data. This paper represents the first adaption of ML methods to snapshot based spoofing detection.