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<p>Spoofing disrupts the normal behaviour of GNSS signals by introducing subtle but measurable changes in key observables. Detecting such attacks requires the receiver to distinguish these abnormal patterns from the naturally stable characteristics of genuine signals. This study evaluates the vulnerability of NavIC to spoofing through a series of controlled static experiments and assesses the performance of commonly used statistical detection techniques. The analysis focuses on pseudorange and Doppler measurements, as these parameters consistently show early deviations when counterfeit signals are introduced. To the best of our knowledge, this is the first study that systematically compares multiple statistical detection frameworks (e.g., the Generalized Likelihood Ratio Test (GLRT), Mahalanobis distance) using NavIC-specific observables such as inter-satellite pseudorange and Doppler variance under spoofing conditions. Among the approaches examined, the Generalized Likelihood Ratio Test (GLRT) demonstrates the most reliable detection capability across different spoofing conditions. Time-series observations further highlight the inherent stability of NavIC’s GEO/GSO satellites, which restricts the effectiveness of spoofing attempts by making departures from normal behaviour more noticeable. The results suggest that NavIC’s constellation geometry naturally supports early spoofing detection, and that coupling these features with machine-learning-based classifiers in future work could enhance robustness against a wider range of spoofing&#xa0;strategies.</p>

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Spoofing Detection in NavIC: Behavioral Analysis of Key Observables and Insights from GEO/GSO Stability

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Sheetal Tanna,
  • Shweta Shah

摘要

Spoofing disrupts the normal behaviour of GNSS signals by introducing subtle but measurable changes in key observables. Detecting such attacks requires the receiver to distinguish these abnormal patterns from the naturally stable characteristics of genuine signals. This study evaluates the vulnerability of NavIC to spoofing through a series of controlled static experiments and assesses the performance of commonly used statistical detection techniques. The analysis focuses on pseudorange and Doppler measurements, as these parameters consistently show early deviations when counterfeit signals are introduced. To the best of our knowledge, this is the first study that systematically compares multiple statistical detection frameworks (e.g., the Generalized Likelihood Ratio Test (GLRT), Mahalanobis distance) using NavIC-specific observables such as inter-satellite pseudorange and Doppler variance under spoofing conditions. Among the approaches examined, the Generalized Likelihood Ratio Test (GLRT) demonstrates the most reliable detection capability across different spoofing conditions. Time-series observations further highlight the inherent stability of NavIC’s GEO/GSO satellites, which restricts the effectiveness of spoofing attempts by making departures from normal behaviour more noticeable. The results suggest that NavIC’s constellation geometry naturally supports early spoofing detection, and that coupling these features with machine-learning-based classifiers in future work could enhance robustness against a wider range of spoofing strategies.