Quantum-Classical Hybrid Framework for Zero-Day Time-Push GNSS Spoofing Detection

2025-08-25

AI Summary

GNSS is vulnerable to time-pushing spoofing attacks, and conventional supervised detection methods struggle against previously unseen zero-day attacks. This paper proposes a quantum-classical hybrid autoencoder (HQC-AE) tailored for static GNSS receivers, which extracts multidimensional features solely from authentic GNSS signals during the tracking phase—enabling unsupervised anomaly detection prior to PNT solution computation. To our knowledge, this is the first application of a quantum-classical autoencoder to GNSS spoofing detection, eliminating reliance on labeled spoofing data. Experimental results demonstrate that HQC-AE achieves an average detection accuracy of 97.71% and a false negative rate of only 0.62% across diverse unknown time-pushing attacks. Under high-complexity spoofing scenarios, it maintains 98.23% accuracy and a 1.85% false negative rate—significantly outperforming state-of-the-art supervised and unsupervised approaches.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: State Estimation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Global Navigation Satellite Systems (GNSS) are critical for Positioning, Navigation, and Timing (PNT) applications. However, GNSS are highly vulnerable to spoofing attacks, where adversaries transmit counterfeit signals to mislead receivers. Such attacks can lead to severe consequences, including misdirected navigation, compromised data integrity, and operational disruptions. Most existing spoofing detection methods depend on supervised learning techniques and struggle to detect novel, evolved, and unseen attacks. To overcome this limitation, we develop a zero-day spoofing detection method using a Hybrid Quantum-Classical Autoencoder (HQC-AE), trained solely on authentic GNSS signals without exposure to spoofed data. By leveraging features extracted during the tracking stage, our method enables proactive detection before PNT solutions are computed. We focus on spoofing detection in static GNSS receivers, which are particularly susceptible to time-push spoofing attacks, where attackers manipulate timing information to induce incorrect time computations at the receiver. We evaluate our model against different unseen time-push spoofing attack scenarios: simplistic, intermediate, and sophisticated. Our analysis demonstrates that the HQC-AE consistently outperforms its classical counterpart, traditional supervised learning-based models, and existing unsupervised learning-based methods in detecting zero-day, unseen GNSS time-push spoofing attacks, achieving an average detection accuracy of 97.71% with an average false negative rate of 0.62% (when an attack occurs but is not detected). For sophisticated spoofing attacks, the HQC-AE attains an accuracy of 98.23% with a false negative rate of 1.85%. These findings highlight the effectiveness of our method in proactively detecting zero-day GNSS time-push spoofing attacks across various stationary GNSS receiver platforms.

Problem

• Detects zero-day GNSS spoofing attacks without prior spoofing data
• Addresses vulnerability of static receivers to time-push manipulation
• Overcomes limitations of supervised learning for novel attack detection

Innovation

• Hybrid Quantum-Classical Autoencoder for spoofing detection
• Trained solely on authentic GNSS signals without spoofed data
• Leverages tracking stage features for proactive detection before PNT