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

TL;DR

This paper introduces SSD, a stealthy backdoor attack on UAV navigation systems that exploits motion states to deceive GNSS and compromise trajectory planning, highlighting new vulnerabilities in UAV cybersecurity.

Contribution

The study reveals how nonlinear UAV motion states influence attack effectiveness and proposes a novel state-triggered backdoor method (SSD) to deceive GNSS systems.

Findings

SSD significantly increases positioning errors

SSD maintains 100% stealth across detectors

Motion states affect attack success and detection probability

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.