GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network

被引:11
|
作者
Sung, Young-Hwa [1 ]
Park, Soo-Jae [1 ]
Kim, Dong-Yeon [1 ]
Kim, Sungho [1 ]
机构
[1] Agcy Def Dev, Yuseong POB 35, Daejeon 34186, South Korea
关键词
GPS spoofing attack; small UAV; deep learning; 1D CNN; SVM;
D O I
10.3390/s22239412
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The navigation of small unmanned aerial vehicles (UAVs), such as quadcopters, significantly relies on the global positioning system (GPS); however, UAVs are vulnerable to GPS spoofing attacks. GPS spoofing is an attempt to manipulate a GPS receiver by broadcasting manipulated signals. A commercial GPS simulator can cause a GPS-guided drone to deviate from its intended course by transmitting counterfeit GPS signals. Therefore, an anti-spoofing technique is essential to ensure the operational safety of UAVs. Various methods have been introduced to detect GPS spoofing; however, most methods require additional hardware. This may not be appropriate for small UAVs with limited capacity. This study proposes a deep learning-based anti-spoofing method equipped with 1D convolutional neural network. The proposed method is lightweight and power-efficient, enabling real-time detection on mobile platforms. Furthermore, the performance of our approach can be enhanced by increasing training data and adjusting the network architecture. We evaluated our algorithm on the embedded board of a drone in terms of power consumption and inference time. Compared to the support vector machine, the proposed method showed better performance in terms of precision, recall, and F-1 score. Flight test demonstrated our algorithm could successfully detect GPS spoofing attacks.
引用
收藏
页数:15
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