Statistical Model and Performance Evaluation of a GNSS Spoofing Detection Method based on the Consistency of Doppler and Pseudorange Positioning Results

Abstract

Global Navigation Satellite System (GNSS) safety issues of have been of concern for some time. Spoofing attacks have received much attention as they can be difficult to detect and have the potential to cause disruption at best and major damage in extremis. To mitigate such threats, a spoofing detection method based on the consistency check of Doppler positioning fixes and pseudorange positioning fixes is proposed. The primary contributions of this paper include establishing a Generalised Likelihood Ratio Test (GLRT)-based statistical detection model for the introduced spoofing detection method and efficiently improving the accuracy of the Doppler positioning method as well as the performance of the detection approach by a modified α-filter-based Doppler smoothing technique. Theoretical performance of the proposed detection model is analysed. Monte Carlo simulations were also conducted to verify the theoretical analysis. Moreover, grounded on the developed test statistic and the optimised threshold, a consistency check module was specifically realised in both software defined and real-time GNSS receivers. Additionally, a Doppler smoothing technique was applied to the receivers. Spoofing attack experiments on both software defined and real-time platforms validated the effectiveness of the statistical spoofing detection model.

Detecting Maritime GPS Spoofing Attacks Based on NMEA Sentence Integrity Monitoring

“…Spread spectrum security code (e.g., [32]) System Navigation message authentication (e.g., [32,33]) L1/L2 power level comparison (e.g., [34,35]) L1/L2 power level code phase comparison (e.g., [34]) Hardware DOA monitoring (e.g., [36,37]) Synthetic antenna array (e.g., [38]) Signal strength monitoring (e.g., [17,39]) Doppler monitoring (e.g., [40,41]) Code and phase rates consistency check (e.g., [34]) TOA monitoring (e.g., [17,42]) PRN code and data bit latency (e.g., [43][44][45]) Auxiliary peak tracking APT (e.g., [18]) Signal quality monitoring (e.g., [46][47][48]) Firmware Distribution analysis of correlator output (e.g., [49,50]) C/N 0 Monitoring (CNM) (e.g., [34,39]) Physical Cross-Check (PCC) (e.g., [51][52][53][54]) Clock Drift Monitoring (CDM) (e.g., [8,18]) Ephemeris Data Validation (EDV) (e.g., [34,55]) Software Pairwise Distance Monitoring (PDM) (e.g., [7,56]) Notation: Effective ( ), semi-effective ( ), and ineffective ( ) GPS spoofing detection regarding individual attacker models defined in Section 3.2, cf. Figure 2.…”

Mitigation of GNSS Time Synchronization Attacks in a Multicorrelator Receiver

“…The single-antenna-based anti-spoofing algorithms were implemented by detecting abnormal changes in the signal parameters caused by counterfeit signals. These methods include received power monitoring (RPM) [9], signal quality monitoring (SQM) [10], [11], carrier-to-noise ratio (C/N0) monitoring [12], and carrier Doppler monitoring [13], [14]. Techniques based on artificial neural networks (NNs) are another recent novel approach for detecting spoofing attacks [15], [16].…”

A Flexible GNSS Spoofer Localization System: Spoofing Discrimination and Localization Method

“…While NMA is not yet available, a single-antenna standalone receiver can defend against spoofing by detecting anomalies in signal features, such as abnormal signal power (Akos, 2012), inconsistency between code-based and carrier-based measurements (Chu et al, 2018), distortion of correlation peaks (Pini et al, 2011), or conflict with spatial information from a moving antenna (Broumandan et al, 2016;Wang et al, 2017). Moreover, one can use external information that is not affected by GNSS spoofing.…”