Abstract
With affordable open-source software-defined radio (SDR) devices, the security of civilian Global Position System (GPS) is at risk of spoofing attacks. Spoofed GPS signals from SDR devices have indicated that spoofed signals have higher values of signal-to-noise ratios (SNRs). Utilizing these values along with other parameters, we propose a machine learning (ML) based GPS spoofing detection system for classifying spoofed signals. To build our detection system, we launch spoofing attacks on a GPS receiver using a low-cost SDR device, LimeSDR, and apply ML algorithms on SNR values and the number of tracked and viewed satellites. A performance comparison between different ML algorithms shows that Random Forest (RF) and Support Vector Machine (SVM) achieve 99.5% accuracy, followed by K-Nearest Neighbors (KNN) (99.4%). To demonstrate easy integration of the algorithm with GPS enabled devices, we develop an Android-based smartphone app that successfully notifies the user about the spoofing signals.
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References
GPS Anti Spoof. https://play.google.com/store/apps/details?id=com.clockwk.GPSAntiSpoof
Woodford, C.: Satellite navigation (2019). https://tinyurl.com/y8wss3wt
DePriest, D.: NMEA Data (2019). https://tinyurl.com/b7jvw
Goavec-Merou, G., Friedt, J., Meyer, F.: GPS spoofing using software defined radio (2019)
Jahan, F., Javaid, A.Y., Sun, W., Alam, M.: GNSSim: an open source GNSS/GPS framework for unmanned aerial vehicular network simulation. ICST Trans. Mob. Commun. Appl. 2(6), e2 (2015)
Lime Microsystems: LimeSDR (2019). https://limemicro.com/products/boards/limesdr/
Manesh, M.R., Kenney, J., Hu, W.C., Devabhaktuni, V.K., Kaabouch, N.: Detection of GPS spoofing attacks on unmanned aerial systems. In: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1–6. IEEE (2019)
Noll, C.E.: The crustal dynamics data information system: a resource to support scientific analysis using space geodesy. Adv. Space Res. 45(12), 1421–1440 (2010)
Panice, G., et al.: A SVM-based detection approach for GPS spoofing attacks to UAV. In: 2017 23rd International Conference on Automation and Computing (ICAC), pp. 1–11 (2017)
Shafiee, E., Mosavi, M., Moazedi, M.: Detection of spoofing attack using machine learning based on multi-layer neural network in single-frequency GPS receivers. J. Navigat. 71(1), 169–188 (2018)
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Campos, J. et al. (2020). A Machine Learning Based Smartphone App for GPS Spoofing Detection. In: Park, N., Sun, K., Foresti, S., Butler, K., Saxena, N. (eds) Security and Privacy in Communication Networks. SecureComm 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 336. Springer, Cham. https://doi.org/10.1007/978-3-030-63095-9_13
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DOI: https://doi.org/10.1007/978-3-030-63095-9_13
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