GNSS Spoofing Detection via Predictable and Unpredictable Correlations
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Solution Overview
Problem
Existing methods struggle to effectively detect zero-delay Security Code Estimation and Replay (SCER) attacks in Global Navigation Satellite System (GNSS) signals, as spoofer synchronization with the authentic signal is difficult to achieve, leading to undetected manipulation of the receiver's position, velocity, and time.
Innovation Solution
A computer-implemented method involving partial correlations between predictable and unpredictable parts of GNSS signals, using metrics R2-R5 to compare with predefined thresholds, to detect spoofing by analyzing the complex-valued partial correlations and minimize time-dependent signal impairments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a spoofer performs zero-delay attacks to synchronize with the authentic signal, then the spoofer can control the target receiver without detection, but the receiver clock stability becomes compromised and detection becomes more difficult
Solution Approach 1:
The patent segments the GNSS signal into predictable parts (known structure) and unpredictable parts (data bits). By separately analyzing the correlation of these segments, the method can detect spoofing attacks even when the spoofer maintains zero-delay synchronization. The unpredictable part's correlation properties differ between authentic and spoofed signals, enabling detection without relying on receiver clock stability.
Solution Approach 2:
Instead of detecting spoofing by monitoring receiver clock stability (which is compromised in zero-delay attacks), the patent inverts the approach by analyzing the signal's intrinsic correlation properties. The method computes correlation metrics directly from the received signal segments, reversing the detection logic from observing receiver behavior to analyzing signal characteristics themselves.
2Reliability
If existing detection methods rely on comparing GNSS signal with alternate sources, then detection capability is provided, but the complexity of the system increases and real-time detection becomes difficult
Solution Approach 1:
The patent enables the GNSS receiver to perform self-detection of spoofing attacks using only the received signal itself, without requiring external alternate sources or additional complex infrastructure. The correlation-based detection method uses the signal's own predictable and unpredictable segments to generate detection metrics, making the system self-sufficient and reducing overall system complexity.
Solution Approach 2:
The patent replaces complex mechanical or infrastructure-based detection systems (such as external reference sources or additional hardware) with a computational signal processing approach. By using digital correlation analysis of the received signal segments, the method substitutes physical complexity with algorithmic processing, simplifying the overall system architecture.
3Measurement precision
If the spoofer tracks and estimates unpredictable bits to generate spoofed signals, then the spoofing accuracy improves, but the detection probability using traditional methods decreases
Solution Approach 1:
The patent applies local quality analysis by examining specific local properties of the signal segments rather than the entire signal. The method focuses on the correlation properties of individual predictable and unpredictable segments, detecting local anomalies that indicate spoofing even when the overall signal accuracy is high. This localized approach enables detection of sophisticated spoofing attacks.
Solution Approach 2:
The patent changes the detection parameter from receiver clock stability to signal segment correlation metrics. By computing correlation values between received segments and expected segments, the method transforms the detection problem into a parameter space where spoofed signals exhibit distinct characteristics, enabling reliable detection even when spoofer accuracy is high.
4Reliability
If randomized correlations and complex-valued metrics are used, then spoofing detection probability increases, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by computing correlations on selected signal segments rather than processing the entire signal continuously. The method divides the signal into predictable and unpredictable parts and performs correlation analysis on these segments, achieving sufficient detection probability without excessive computational effort. This selective processing balances detection performance with computational complexity.
Data Source
AI summary
A computer-implemented method is for detecting Global Navigation Satellite System (GNSS) signal spoofing. The method includes storing sample sequences of the predictable part and of the unpredictable part of a GNSS signal at a GNSS receiver. The predictable part includes predictable bits and the unpredictable part includes unpredictable bits. The value of the unpredictable bits from which the unpredictable sample sequences are extracted is verified. A first and a second partial correlation between the unpredictable, respectively predictable, sample sequences and a locally stored GNSS signal replica are computed. A predefined metric from the complex valued partial correlations is calculated. The predefined metric is compared with a predefined threshold value. In a zero-delay replay attack, the spoofer estimates the unpredictable bits introduced by a GNSS authentication protocol and introduces distortion into the signal. Detecting this distortion indicates whether the signal under analysis is being spoofed or is authentic.