Intelligent UAV Spoofing Detection Method

Main Article Content

Denys Voloshyn
Serhii Bulba


The paper presents an intelligent method for detecting UAV spoofing. A distinctive feature of the method is the use of subtrajectory calculation technology based on visual odometry subtrajectories and GPS positions in a sliding window, taking into account the intelligent estimation of the optical flow and the formation of UAV “Ego-movement” descriptors. In the course of the study, an analysis and comparative studies of a wide range of UAV spoofing methods were carried out, the most frequently recommended and practically used methods were identified. The conclusion is made about the relevance of the problems of GPS spoofing. The analysis of methods of protection against UAV GPS spoofing has been carried out. Promising directions for intelligent detection of UAV spoofing using methods and means of visual odometry are identified. In the course of studying methods for fixing input data, an approach was proposed for estimating the optical flow using a sliding window. At the same time, the need for intelligent processing of input data is argued. The estimation of the optical flow and the formation of descriptors was carried out using recurrent convolutional neural networks. As a result, a block diagram of the UAV spoofing detection method was developed. This allowed us to study the developed method. The results of the experiment for two spoofing scenarios showed the efficiency of estimating the positions of at least two of the three indicators under the conditions of using sliding windows of size 15 or more, with a time delay of half the window size.

Article Details

How to Cite
Voloshyn, D., & Bulba , S. . (2022). Intelligent UAV Spoofing Detection Method. Advanced Information Systems, 6(1), 88–96.
Methods of information systems protection
Author Biographies

Denys Voloshyn, National Technical University "Kharkiv Polytechnic Institute", Kharkiv, Ukraine

graduate student of Computer Engineering and Programming Department

Serhii Bulba , National Technical University "Kharkiv Polytechnic Institute", Kharkiv, Ukraine

Candidate of Technical Sciences, Associate Professor of Computer Engineering and Programming Department


Jafarnia-Jahromi, Ali, Broumandan, Ali, Nielsen, J. & Lachapelle, G. (2012), “GPS Vulnerability to Spoofing Threats and a Review of Antispoofing Techniques”, Int, Journal of Navigation and Observation, doi:

Lawal, A.B. (2020), How to Design GPS/GNSS Receivers Books 2, 3, 4 & 5, URL: books?id=RXANEAAAQBAJ&printsec=frontcover&hl=ru&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false.

Shepard, D.P.; Bhatti, J.A.; Humphreys, T.E. & Fansler, A.A. (2012), “Evaluation of smart grid and civilian UAV vulnerability to GPS spoofing attacks”, Radionavigation Laboratory Conf. Proc., The University of Texas at Austin: Austin, TX, USA,.

Kerns, A.J.; Shepard, D.P.; Bhatti, J.A.; Humphreys, T.E. Unmanned Aircraft Capture and Control Via GPS Spoofing. J. Field Robot. 2014, 31, 617–636.

He, D., Qiao, Y., Chen, S., Du, X., Chen, W., Zhu, S. and Guizani, M. (2019), “A Friendly and Low-Cost Technique for Capturing Non-Cooperative Civilian Unmanned Aerial Vehicles”, IEEE Netw., 33, pp. 146–151.

Guo, Y., Wu, M., Tang, K., Tie, J. and Li, X. (2019), “Covert Spoofing Algorithm of UAV based on GPS/INS Integrated Navigation”, IEEE Trans. Veh. Technol.

Broumandan, A.; Jafarnia-Jahromi, A.; Daneshmand, S.; Lachapelle, G. (2016), “Overview of Spatial Processing Approaches for GNSS Structural Interference Detection and Mitigation”, Proc. IEEE, 104, pp. 1246–1257.

Milaat, F.A. and Liu, H. (2018), “Decentralized Detection of GPS Spoofing”, IEEE Commun. Lett., 22, pp. 1256–1259.

Sun, C.; Cheong, J.W.; Dempster, A.G.; Zhao, H.; Demicheli, L.; Feng,W. A (2018), “New Signal Quality Monitoring Method for Anti-spoofing”, China Satellite Navigation Conference (CSNC) 2018 Proceedings, Springer, Singapore, pp. 221–231.

Humphreys, T., Bhatti, J. and Ledvina, B. (2010), “The GPS Assimilator: A method for upgrading existing GPS user equipment to improve accuracy, robustness, and resistance to spoofing”, Radionavigation Laboratory Conference Proceedings, Proceedings of the ION GNSS Conference, Portland, OR, USA, The University of Texas at Austin: Austin, TX, USA,.

Oligeri, G., Sciancalepore, S., Ibrahim, O.A. and Pietro, R.D. (2019), “Drive me not: GPS spoofing detection via cellular network: (architectures, models, and experiments”, Proc. of the 12th Conf. on Security and Privacy in Wireless and Mobile Networks, Miami, FL, USA, 14–17 May; pp. 12–22.

Qiao, Y., Zhang, Y. and Du, X. A. (2017), “Vision-Based GPS-Spoofing Detection Method for Small UAVs”, Proceedings of the 2017 13th International Conference on Computational Intelligence and Security (CIS), Hong Kong, China, pp. 312–316.

Chowdhary, G., Johnson, E.N., Magree, D., Wu, A. and Shein, A. (2013), “GPS-denied indoor and outdoor monocular vision aided navigation and control of unmanned aircraft”, J. Field Robot, 30, pp. 415–438.

Gonzalez, R.; Rodriguez, F.; Guzman, J.L.; Pradalier, C. and Siegwart, R. (2012), “Combined visual odometry and visual compass for mobile robots localization”, Robotica, 30, pp. 865–878.

Scaramuzza, D. and Siegwart, R. (2008), “Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles”, IEEE Trans. Robot, 24, pp. 1015–1026.

Sun, Deqing, Yang, Xiaodong, Liu, Ming-Yu & Kautz, Jan (2018), PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume”, 2018 IEEE/CVF Conf. on Comp. Vision and Pattern Recognition, pp. 8934-8943.

Gerardus, Blokdyk (2018), Recurrent neural network: Real Life Actions Paperback, 132 p.

Varshosaz, Masood, Afary, Ali Reza, Mojaradi, Barat, Saadatseresht, Mohammad & Ghanbari Parmehr, Ebadat (2019), “Spoofing Detection of Civilian UAVs Using Visual Odometry”, ISPRS International Journal of Geo-Information, 9, doi: