SSD: A State-based Stealthy Backdoor Attack For Navigation System in UAV Route Planning

2025-02-27

AI Summary

GNSS-based navigation systems for unmanned aerial vehicles (UAVs) are vulnerable to spoofing attacks during path planning, yet the influence of vehicle motion dynamics on attack efficacy and detection robustness remains unexplored. Method: We propose a motion-state-triggered stealthy backdoor attack: a state-aware trigger mechanism is designed leveraging nonlinear kinematic features to enable task-level targeted interference; backdoor injection and GNSS signal manipulation are made motion-sensitive, enabling evasion of three state-of-the-art anomaly detectors—without inducing abnormal velocity or acceleration signatures. Contribution/Results: Experiments demonstrate 100% stealthiness while significantly amplifying positioning errors, increasing path-planning failure rates by over 80%. The attack is validated in a realistic flight-control simulation environment, confirming both practical feasibility and operational impact on UAV autonomy.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Unmanned aerial vehicles (UAVs) are increasingly employed to perform high-risk tasks that require minimal human intervention. However, UAVs face escalating cybersecurity threats, particularly from GNSS spoofing attacks. While previous studies have extensively investigated the impacts of GNSS spoofing on UAVs, few have focused on its effects on specific tasks. Moreover, the influence of UAV motion states on the assessment of network security risks is often overlooked. To address these gaps, we first provide a detailed evaluation of how motion states affect the effectiveness of network attacks. We demonstrate that nonlinear motion states not only enhance the effectiveness of position spoofing in GNSS spoofing attacks but also reduce the probability of speed-related attack detection. Building upon this, we propose a state-triggered backdoor attack method (SSD) to deceive GNSS systems and assess its risk to trajectory planning tasks. Extensive validation of SSD's effectiveness and stealthiness is conducted. Experimental results show that, with appropriately tuned hyperparameters, SSD significantly increases positioning errors and the risk of task failure, while maintaining 100% stealth across three state-of-the-art detectors.

Problem

• UAV cybersecurity threats from GNSS spoofing
• Impact of UAV motion states on attack effectiveness
• State-triggered backdoor attack method (SSD) for GNSS deception

Innovation

• State-triggered backdoor attack
• GNSS system deception
• Motion state impact analysis