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GNSS Spoofing and Jamming Detection and Mitigation in Smart City Architecture
Subject area: Science,Engineering and Technology · Area of research: Cybersecurity in Space Technology
DOI: 10.64388/IREV10I3-1722902
Abstract
Global Navigation Satellite Systems (GNSS) have become an invisible utility underpinning nearly every subsystem of the modern smart city, from adaptive traffic control and autonomous vehicles to smart-grid timing, 5G/6G synchronization, and emergency dispatch. Because civil GNSS signals are transmitted at very low power over publicly documented formats, they are structurally vulnerable to jamming, which denies service through radio-frequency noise, and spoofing, which deceives receivers with counterfeit signals that appear authentic. This paper examines the role of GNSS within smart city architecture, surveys the jamming and spoofing threat landscape, and reviews current detection techniques spanning signal-level monitoring, cryptographic authentication, spatial antenna-array processing, and data-driven machine learning approaches. It then reviews mitigation strategies, including controlled reception pattern antennas, navigation message authentication, sensor fusion, and crowdsourced interference monitoring, before proposing a six-layer secure detection and mitigation architecture tailored to the distributed, multi-stakeholder nature of smart cities. The paper concludes by identifying open research gaps, including adversarial machine learning, urban-canyon testing deficits, cross-jurisdictional governance, and the cost of hardened receiver hardware at municipal scale.
Keywords
GNSS, GPS spoofing, jamming, smart city, positioning navigation and timing, cybersecurity, critical infrastructure
References
[1] Altaweel, A., Mukkath, H., & Kamel, I. (2023). GPS spoofing attacks in FANETs: A systematic literature review. IEEE Access, 11, 55233–55280. https://doi.org/10.1109/ACCESS.2023.3281731
[2] Bose, S. C. (2021). GPS spoofing detection by neural network machine learning. IEEE Aerospace and Electronic Systems Magazine, 37(6), 18–31.
[3] Carroll, J. V. (2003). Vulnerability assessment of the U.S. transportation infrastructure that relies on the Global Positioning System. Journal of Navigation, 56(2), 185–193. https://doi.org/10.1017/S0373463303002273
[4] Dang, Y., Benzaïd, C., Yang, B., Taleb, T., & Shen, Y. (2022). Deep-ensemble-learning-based GPS spoofing detection for cellular-connected UAVs. IEEE Internet of Things Journal, 9(24), 25068–25085.
[5] Data Center Dynamics. (2024, April 30). Why GNSS-independent time sync is crucial for critical national services. https://www.datacenterdynamics.com/en/opinions/why-gnss-independent-time-sync-is-crucial-for-critical-national-services/
[6] Deng, Y., Zhang, T., Lou, G., Zheng, X., Jin, J., & Han, Q.-L. (2021). Deep learning-based autonomous driving systems: A survey of attacks and defenses. IEEE Transactions on Industrial Informatics, 17(12), 7897–7912. https://doi.org/10.1109/TII.2021.3071405
[7] European Commission, Directorate-General for Defence Industry and Space. (2025, September 8). Observer: How Galileo OSNMA helps counter GNSS spoofing. https://defence-industry-space.ec.europa.eu/observer-how-galileo-osnma-helps-counter-gnss-spoofing-2025-09-08_en
[8] Ghanbarzadeh, A., Soleimani, M., & Soleimani, H. (2025). GNSS/GPS spoofing and jamming identification using machine learning and deep learning. arXiv. https://arxiv.org/abs/2501.02352
[9] GPS World. (2026, June 22). Timing matters: The critical role of GNSS-resilient systems in modern infrastructure. https://www.gpsworld.com/timing-matters-the-critical-role-of-gnss-resilient-systems-in-modern-infrastructure/
[10] Humphreys, T. E. (2013). Detection strategy for cryptographic GNSS anti-spoofing. IEEE Transactions on Aerospace and Electronic Systems, 49(2), 1073–1090. https://doi.org/10.1109/TAES.2013.6494400
[11] International Association of Marine Aids to Navigation and Lighthouse Authorities. (2025, December 17). Open Service Navigation Message Authentication (OSNMA). https://www.iala.int/e-bulletin/open-service-navigation-message-authentication-osnma/
[12] Islam, R., Bose, R., Roy, S., Khan, A. A., et al. (2025). Decentralized trust framework for smart cities: A blockchain-enabled cybersecurity and data integrity model. Scientific Reports, 15, Article 06405. https://doi.org/10.1038/s41598-025-06405-y
[13] Keysight Technologies. (2024, November 29). The best anti-jam solutions for GPS/GNSS resilience. https://www.keysight.com/blogs/en/tech/positioning-navigation-and-timing/crpa-antennas-explained-choosing-and-testing-the-best-anti-jam-solutions-for-gps-gnss-resilience
[14] Lu, C., Lu, Z., Liu, Z., Huang, L., & Chen, F. (2024). Overview of satellite navigation spoofing and anti-spoofing techniques. Frontiers in Physics, 12, Article 1428544. https://doi.org/10.3389/fphy.2024.1428544
[15] Mohanty, A., & Gao, G. (2024). A survey of machine learning techniques for improving Global Navigation Satellite Systems. EURASIP Journal on Advances in Signal Processing, 2024, Article 73. https://doi.org/10.1186/s13634-024-01167-7
[16] Morales Ferre, R., de la Fuente, A., & Lohan, E. S. (2019). Jammer classification in GNSS bands via machine learning algorithms. Sensors, 19(22), Article 4841. https://doi.org/10.3390/s19224841
[17] Nayfeh, M., Li, Y., Al Shamaileh, K., Devabhaktuni, V., & Kaabouch, N. (2023). Machine learning modeling of GPS features with applications to UAV location spoofing detection and classification. Computers & Security, 126, Article 103085.
[18] Psiaki, M. L., & Humphreys, T. E. (2016). GNSS spoofing and detection. Proceedings of the IEEE, 104(6), 1258–1270. https://doi.org/10.1109/JPROC.2016.2526658
[19] Radoš, K., Brkić, M., & Begušić, D. (2024). Recent advances on jamming and spoofing detection in GNSS. Sensors, 24(13), Article 4210. https://doi.org/10.3390/s24134210
[20] Sathaye, H., LaMountain, G., Closas, P., & Ranganathan, A. (2022). SemperFi: Anti-spoofing GPS receiver for UAVs. In Proceedings of the 2022 Network and Distributed System Security Symposium. Internet Society.
[21] Sathaye, H., Strohmeier, M., Lenders, V., & Ranganathan, A. (2022). An experimental study of GPS spoofing and takeover attacks on UAVs. In Proceedings of the 31st USENIX Security Symposium (pp. 3345–3362). USENIX Association.
[22] Scott, L. (2011). J911: The case for fast jammer detection and location using crowdsourcing approaches. In Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2011) (pp. 1931–1940). Institute of Navigation.
[23] Septentrio. (2026). How GNSS receivers empower smart cities. https://www.septentrio.com/en/learn-more/insights/how-gnss-receivers-empower-smart-cities
[24] Shafique, A., Mehmood, A., & Elhadef, M. (2021). Detecting signal spoofing attack in UAVs using machine learning models. IEEE Access, 9, 93803–93815. https://doi.org/10.1109/ACCESS.2021.3089847
[25] Shen, J., Won, J. Y., Chen, Z., & Chen, Q. A. (2020). Drift with devil: Security of multi-sensor fusion based localization in high-level autonomous driving under GPS spoofing. In Proceedings of the 29th USENIX Security Symposium (pp. 931–948). USENIX Association.
[26] Son, Y., Shin, H., Kim, D., Park, Y., Noh, J., Choi, K., Choi, J., & Kim, Y. (2015). Rocking drones with intentional sound noise on gyroscopic sensors. In Proceedings of the 24th USENIX Security Symposium (pp. 881–896). USENIX Association.
[27] Strizic, L., Akos, D. M., & Lo, S. (2018). Crowdsourcing GNSS jamming detection and localization. In Proceedings of the 2018 International Technical Meeting of the Institute of Navigation (pp. 626–641). Institute of Navigation.
[28] Swinney, C. J., & Woods, J. C. (2021). Raw IQ dataset for GNSS GPS jamming signal classification [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4610850
[29] Tanıl, Ç., Khanafseh, S., Joerger, M., & Pervan, B. (2017). An INS monitor to detect GNSS spoofers capable of tracking vehicle position. IEEE Transactions on Aerospace and Electronic Systems, 54(1), 131–143. https://doi.org/10.1109/TAES.2017.2739924
[30] Taoglas. (2025, October 30). Countering GNSS jamming and spoofing for aerospace and defense applications. https://www.taoglas.com/blogs/countering-gnss-jamming-and-spoofing-for-aerospace-and-defense-applications/
[31] Wei, X., Wang, Y., & Sun, C. (2022). PerDet: Machine-learning-based UAV GPS spoofing detection using perception data. Remote Sensing, 14(19), Article 4925. https://doi.org/10.3390/rs14194925
[32] Wesson, K. D., Evans, B. L., & Humphreys, T. E. (2013). A combined symmetric difference and power monitoring GNSS anti-spoofing technique. In 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 217–220). IEEE. https://doi.org/10.1109/GlobalSIP.2013.6736863
[33] Wesson, K. D., Gross, J. N., Humphreys, T. E., & Evans, B. L. (2017). GNSS signal authentication via power and distortion monitoring. IEEE Transactions on Aerospace and Electronic Systems, 54(2), 739–754. https://doi.org/10.1109/TAES.2017.2735618
[34] Windward. (2026, June 25). What is GPS jamming? https://windward.ai/glossary/what-is-gps-jamming/
[35] Zeng, K. C., Shu, Y., Liu, S., Dou, Y., & Yang, Y. (2017). A practical GPS location spoofing attack in road navigation scenario. In Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications (pp. 85–90). ACM. https://doi.org/10.1145/3032970.3032983
[36] Zidan, J., Adegoke, E. I., Kampert, E., Birrell, S. A., Ford, C. R., & Higgins, M. D. (2020). GNSS vulnerabilities and existing solutions: A review of the literature. IEEE Access, 9, 153960–153976. https://doi.org/10.1109/ACCESS.2020.3010353
How to cite this paper
@article{1722902,
author = {Zulaihat Mukhtar, Jennifer Nanko Sambo, Rekiya Sule, Bulus Kefas, Saeed Baba Ahmad; Aisha Yakubu},
title = {GNSS Spoofing and Jamming Detection and Mitigation in Smart City Architecture},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {387-396},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722902.pdf},
abstract = {Global Navigation Satellite Systems (GNSS) have become an invisible utility underpinning nearly every subsystem of the modern smart city, from adaptive traffic control and autonomous vehicles to smart-grid timing, 5G/6G synchronization, and emergency dispatch. Because civil GNSS signals are transmitted at very low power over publicly documented formats, they are structurally vulnerable to jamming, which denies service through radio-frequency noise, and spoofing, which deceives receivers with counterfeit signals that appear authentic.
This paper examines the role of GNSS within smart city architecture, surveys the jamming and spoofing threat landscape, and reviews current detection techniques spanning signal-level monitoring, cryptographic authentication, spatial antenna-array processing, and data-driven machine learning approaches. It then reviews mitigation strategies, including controlled reception pattern antennas, navigation message authentication, sensor fusion, and crowdsourced interference monitoring, before proposing a six-layer secure detection and mitigation architecture tailored to the distributed, multi-stakeholder nature of smart cities.
The paper concludes by identifying open research gaps, including adversarial machine learning, urban-canyon testing deficits, cross-jurisdictional governance, and the cost of hardened receiver hardware at municipal scale.},
keywords = {GNSS, GPS spoofing, jamming, smart city, positioning navigation and timing, cybersecurity, critical infrastructure},
month = {September},
doi = {https://doi.org/10.64388/IREV10I3-1722902}
}