Real-Time Bayesian Detection of Drift-Evasive GNSS Spoofing in Reinforcement Learning Based UAV Deconfliction

2025/07/15 by Deepak Kumar Panda, Panda, Deepak Kumar, Weisi Guo +1
Computer Science · Engineering · #FOS: Computer and information sciences #Guidance and Control Systems #Machine Learning (cs.LG) #Robotic Path Planning Algorithms #Robotics and Sensor-Based Localization

paper · pdf · doi:10.48550/arxiv.2507.11173

openalex publication_date 2025/07/15 · openalex created_date 2025/10/08 · openalex updated_date 2026/07/28

Abstract

Autonomous unmanned aerial vehicles (UAVs) rely on global navigation satellite system (GNSS) pseudorange measurements for accurate real-time localization and navigation. However, this dependence exposes them to sophisticated spoofing threats, where adversaries manipulate pseudoranges to deceive UAV receivers. Among these, drift-evasive spoofing attacks subtly perturb measurements, gradually diverting the UAVs trajectory without triggering conventional signal-level anti-spoofing mechanisms. Traditional distributional shift detection techniques often require accumulating a threshold number of samples, causing delays that impede rapid detection and timely response. Consequently, robust temporal-scale detection methods are essential to identify attack onset and enable contingency planning with alternative sensing modalities, improving resilience against stealthy adversarial manipulations. This study explores a Bayesian online change point detection (BOCPD) approach that monitors temporal shifts in value estimates from a reinforcement learning (RL) critic network to detect subtle behavioural deviations in UAV navigation. Experimental results show that this temporal value-based framework outperforms conventional GNSS spoofing detectors, temporal semi-supervised learning frameworks, and the Page-Hinkley test, achieving higher detection accuracy and lower false-positive and false-negative rates for drift-evasive spoofing attacks.

Citations

• Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments
• Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks
• Class-Aware Contrastive Semi-Supervised Learning
• Known Vulnerabilities of Global Navigation Satellite Systems, Status, and Potential Mitigation Techniques

Cited by

• Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments
• Meta Policy Switching for Secure UAV Deconfliction in Adversarial Airspace

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