mlsanityData to 14 Oct 2026

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

arXiv 2507.11173 ↗First posted 15 Jul 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 21 references that are in the map. First posted on arXiv 15 Jul 2025.

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  1. 2025Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments26 Jun

Other references · 3

  1. 2018Scikit-Multiflow: A Multi-output Streaming Frameworkcited by 70
  2. 2022Class-Aware Contrastive Semi-Supervised Learningcited by 62
  3. 2025Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attackscited by 1

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  4. 2026Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing
  5. 2026Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning
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  7. 2025Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions
  8. 2025Bayesian Active Inference for Intelligent UAV Anti-Jamming and Adaptive Trajectory Planning