GPS Spoofing Detection by Neural Network Machine Learning

Abstract

GPS today is ubiquitous. It provides real-time positioning, navigation, and timing (PNT) data for countless military and civilian users worldwide. Yet, a proliferation of GPS degrading and denying devices threatens GPS PNT capabilities. The biggest threat is spoofing, which deceives a GPS receiver into accepting false signals as genuine. Current antispoofing techniques are highly dependent on specific spoofing scenarios, choice of mathematical models, values of thresholds, and often require significant hardware and/or software modifications to existing GPS receivers. Efforts are also underway to replace GPS with alternate sources. However, no such sensor can match the availability, accuracy, and global utility of GPS. This article examines an alternate antispoofing method using neural networks. The supervised machine learning technique uses well-understood GPS observables such as pseudorange, carrier phase, Doppler shift, and carrier-to-noise density ratio to distinguish between authentic and spoofed signals. Network architecture, training sample size, number of hidden layers, distribution of neurons between hidden layers, and number of hidden neurons are examined. Results show that the proposed method is capable of detecting spoofing signals with a high probability of correct classification and a low probability of misclassification.