GPS Spoofing Attack Recognition for UAVs With Limited Samples
Dingchen She, Wei Wang, Zhisheng Yin, Jiaqi Wang, Haifeng Shan
Abstract
As a malicious attack targeting on the GPS receiver, GPS spoofing attack interferes the normal received satellite signal by reproducing or relaying the signal, resulting in severe position deviation. Such attack has posed significant security threat to unmanned-aerial-vehicles (UAVs), especially in the era of low-altitude economics. However, due to the similarity of the spoofing and intended signal, and the presence of noise, accurate detection and recognition of GPS spoofing attack still remains a challenging issue, particularly in the case of limited samples. In this article, we apply the AdaBoost-CNN algorithm, which combines multiple weak convolutional neural network (CNN) classifiers into a strong classification model, to achieve GPS spoofing attack recognition. To further improve the recognition accuracy when there are very limited samples, we improve the AdaBoost-CNN algorithm by transferring previous network parameters to subsequent CNN. Both simulated and real measurement data are employed to verify the effectiveness of the proposed scheme. It is shown that the recognition accuracy can reach up to 93.75% and 95.83% with 160 simulated samples and 120 measured samples, respectively.