Journal
REMOTE SENSING
Volume 14, Issue 19, Pages -Publisher
MDPI
DOI: 10.3390/rs14194925
Keywords
UAV; GPS spoofing attacks; GPS spoofing detection; perception data; machine learning
Categories
Funding
- Natural Science Basic Research Program of Shaanxi [2021JQ-207]
- Fundamental Research Funds for the Central Universities [XJS211506]
- National Natural Science Foundation of China [62002278]
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This paper proposes a perception-data-based UAV GPS spoofing detection method called PerDet. By utilizing data from multiple sensors and running machine learning algorithms, PerDet is able to effectively detect GPS spoofing signals with a high detection rate.
To ensure that unmanned aerial vehicle (UAV) positioning is not affected by GPS spoofing signals, we propose PerDet, a perception-data-based UAV GPS spoofing detection approach utilizing machine learning algorithms. Based on the principle of the position estimation process and attitude estimation process, we choose the data gathered by the accelerometer, gyroscope, magnetometer, GPS and barometer as features. Although these sensors have different shortcomings, their variety makes sure that the selected perception data can compensate for each other. We collect the experimental data through real flights, which make PerDet more practical. Furthermore, we run various machine learning algorithms on our dataset and select the most effective classifier as the detector. Through the performance evaluation and comparison, we demonstrate that PerDet is better than existing methods and is an effective method with a detecting rate of 99.69%. For a fair comparison, we reproduce the existing method and run it on our dataset to compare the performance between this method and our PerDet approach.
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