TY - GEN
T1 - DeepSIM
T2 - 36th Annual Computer Security Applications Conference, ACSAC 2020
AU - Xue, Nian
AU - Niu, Liang
AU - Hong, Xianbin
AU - Li, Zhen
AU - Hoffaeller, Larissa
AU - Pöpper, Christina
N1 - Funding Information:
The authors would like to thank Liangliang Cao, Qingtao Chen, Youtian Zhang, and Zhenjun Zhao for their helpful comments and the anonymous reviewers for their insightful suggestions. This work was supported by the Center for Cyber Security at New York University Abu Dhabi (NYUAD). This research was carried out on the High Performance Computing resources at NYUAD.
Publisher Copyright:
© 2020 ACM.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - Unmanned Aerial Vehicles (UAVs), better known as drones, have significantly advanced fields such as aerial surveillance, military reconnaissance, cadastral surveying, disaster monitoring, and delivery services. However, UAVs rely on civilian (unauthenticated) GPS for navigation which can be trivially spoofed. In this paper, we present DeepSIM, a satellite imagery matching approach to detect GPS spoofing attacks against UAVs based on deep learning. We make use of the camera(s) a typical UAV is equipped with, and present a system that compares historical satellite images of its GPS-based position (spaceborne photography) with real-time aerial images from its cameras (airborne imagery). Historical images are taken from, e. g., Google Earth or NASA WorldWind. To detect GPS spoofing attacks, we investigate different deep neural network models that compare the real-time camera images with the historical satellite images. To train and test the models, we have constructed the SatUAV dataset (consisting of 967 image pairs), partially by using real UAVs such as the DJI Phantom 4 Advanced. Real-world experimental results show that our best model has a success rate of about 95% in detecting GPS spoofing attacks within less than 100 milliseconds. Our approach does not require any modification of the existing GPS infrastructures and relies only on public satellite imagery, making it a practical solution for many everyday scenarios.
AB - Unmanned Aerial Vehicles (UAVs), better known as drones, have significantly advanced fields such as aerial surveillance, military reconnaissance, cadastral surveying, disaster monitoring, and delivery services. However, UAVs rely on civilian (unauthenticated) GPS for navigation which can be trivially spoofed. In this paper, we present DeepSIM, a satellite imagery matching approach to detect GPS spoofing attacks against UAVs based on deep learning. We make use of the camera(s) a typical UAV is equipped with, and present a system that compares historical satellite images of its GPS-based position (spaceborne photography) with real-time aerial images from its cameras (airborne imagery). Historical images are taken from, e. g., Google Earth or NASA WorldWind. To detect GPS spoofing attacks, we investigate different deep neural network models that compare the real-time camera images with the historical satellite images. To train and test the models, we have constructed the SatUAV dataset (consisting of 967 image pairs), partially by using real UAVs such as the DJI Phantom 4 Advanced. Real-world experimental results show that our best model has a success rate of about 95% in detecting GPS spoofing attacks within less than 100 milliseconds. Our approach does not require any modification of the existing GPS infrastructures and relies only on public satellite imagery, making it a practical solution for many everyday scenarios.
KW - deep learning
KW - GPS spoofing detection
KW - neural networks
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85098068579&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098068579&partnerID=8YFLogxK
U2 - 10.1145/3427228.3427254
DO - 10.1145/3427228.3427254
M3 - Conference contribution
AN - SCOPUS:85098068579
T3 - ACM International Conference Proceeding Series
SP - 304
EP - 319
BT - Proceedings - 36th Annual Computer Security Applications Conference, ACSAC 2020
PB - Association for Computing Machinery
Y2 - 7 December 2020 through 11 December 2020
ER -