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Trajectory-Driven Deep Learning for UAV Location Integrity Checks
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DOI：10.1109/ACCESS.2024.3507637.png)
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Abstract
En 中文
While unmanned aerial vehicles (UAVs) are increasingly utilized in many domains, there is a growing concern about location integrity for securely deploying and managing the vehicles. A body of studies tackled this problem, e.g., using hardware sensors, cryptographic mechanisms, and machine learning (ML) approaches, but they concentrate primarily on GPS signal-related information (e.g., jamming and noise). In this study, we take a different approach that performs the checks by analyzing actual movement information represented with a sequence of flight records. This sequence-based approach keeps track of location updates across the flight path ('trajectory') rather than relying on point-wise signal-specific features to test the validity of the location information based on a single observation. Specifically, we define a set of attributes effectively capturing the movement of aerial vehicles over time, resulting in eight features with no signal-dependent information. We then present our deep sequence method, implemented on top of either a recurrent neural network (RNN) or a Transformer with a backend classifier, performing integrity checks with the newly defined feature set. Our extensive experimental results support the feasibility of our trajectory-based analysis approach, showing up to 98.9% classification performance with negligible false positive rates (lower than 1%) for ensuring location consistency (even without referring to the GPS signal-specific information).
Keywords:
Global Positioning System
Autonomous aerial vehicles
Trajectory
Feature extraction
Accuracy
Transformers
Recurrent neural networks
Noise
Long short term memory
Jamming
Unmanned aerial vehicles
location integrity
location spoofing
trajectory
feature extraction
sequence-based analysis
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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