№1, 2022


Fargana J. Abdullayeva, Orkhan V. Valikhanli

As in other vehicles, unmanned aerial vehicles (UAV) mainly use GPS (Global Positioning System) for the provision of navigation. Non-execution of necessary measures on UAV, availability of the devices used in the process of attack may cause GPS spoofing attack on UAV. The quick detection of the attack plays an important role in obtaining safety precautions. The use of artificial neural networks in the detection of such attacks is very convenient. Therefore, in the article new approach based on convolutional neural network (CNN) method is proposed in order to detect GPS spoofing attack. The new approach has been developed for two different types of UAVs. As a result of conducted experiments, high-accuracy detection of GPS spoofing attack has been provided (pp.3-8).

Keywords: UAV, GPS spoofing, CNN, Intrusion detection system
DOI : 10.25045/jpit.v13.i1.01

Borhani-Darian, P., Li, H., Wu, P., & Closas, P. (2020). Deep Neural Network Approach to Detect GNSS Spoofing Attacks. Proceedings Of The 33Rd International Technical Meeting Of The Satellite Division Of The Institute Of Navigation (ION GNSS+ 2020), Manassas, Virginia, USA, September 2020, (pp. 3241-3252). https://doi.org/10.33012/2020.17537

Brownlee, J. (2016). What is a confusion matrix in machine learning. Machine Learning Mastery. https://machinelearningmastery.com/confusion-matrix-machine-learning

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874. https://doi.org/10.1016/j.patrec.2005.10.010

Manesh, M., Kenney, J., Hu, W., Devabhaktuni, V., & Kaabouch, N. (2019). Detection of GPS Spoofing Attacks on Unmanned Aerial Systems. 16Th IEEE Annual Consumer  Communications & Networking Conference  (CCNC),   Piscataway, New Jersey, USA, January 2019 (pp. 1-6).  https://doi.org/10.1109/ccnc.2019.8651804

Park, K., Park, E., & Kim, H. (2020). Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort. Information Security Applications, 45-58. https://doi.org/10.1007/978-3-030-65299-9_4

Psiaki, M., & Humphreys, T. (2016). GNSS Spoofing and Detection. Proceedings Of The IEEE, 104(6), 1258-1270. https://doi.org/10.1109/jproc.2016.2526658

Riahi Manesh, M., & Kaabouch, N. (2019). Cyber-attacks on unmanned aerial system networks: Detection, countermeasure, and future research directions. Computers & Security, 85, 386-401. https://doi.org/10.1016/j.cose.2019.05.003

Semanjski, S., Semanjski, I., De Wilde, W., & Muls, A. (2020). Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I. Sensors, 20(4), 1171. https://doi.org/10.3390/s20041171

Shafiee, E., Mosavi, M., & Moazedi, M. (2017). Detection of Spoofing Attack using Machine Learning based on Multi-Layer Neural Network in Single-Frequency GPS Receivers. Journal Of Navigation, 71(1), 169-188. https://doi.org/10.1017/s0373463317000558

Whelan, J., Sangarapillai, T., Minawi, O., Almehmadi, A., & El-Khatib, K. (2020). UAV Attack Dataset. IEEE Dataport. https://dx.doi.org/10.21227/00dg-0d12 

Xiao, K., Zhao, J., He, Y., Li, C., & Cheng, W. (2019). Abnormal Behavior Detection Scheme of UAV Using Recurrent Neural Networks. IEEE Access, 7, 110293-110305. https://doi.org/10.1109/access.2019.2934188

Yağdereli, E., Gemci, C., & Aktaş, A. (2015). A study on cyber-security of autonomous and unmanned vehicles. The Journal Of Defense Modeling And Simulation: Applications, Methodology, Technology, 12(4), 369-381. https://doi.org/10.1177/1548512915575803