Adaptive Neural Networks for Mitigating GPS Spoofing Attacks in Unmanned Aerial Vehicles

Alejandro SalgadoYezid Donoso2026-03-222026-03-22202510.15837/ijccc.2025.3.7051https://doi.org/10.15837/ijccc.2025.3.7051https://andeanlibrary.org/handle/123456789/77346Unmanned Aerial Vehicles (UAVs), or drones, have gained global attention over the past decades for their ability to perform diverse tasks without direct human intervention. With advances in artificial intelligence, UAV systems have not only improved, but also become more vulnerable to an increasing number of cyberattacks. This paper presents a study on detecting integrity failures in UAVs, focusing specifically on the GPS system. Theoretical background, methodology, and results are discussed, providing a comprehensive overview of adaptive neural networks developed to detect and mitigate the impact of these vulnerabilities in simulated scenarios.enGlobal Positioning SystemSpoofing attackComputer scienceArtificial neural networkComputer networkReal-time computingEnvironmental scienceAdaptive Neural Networks for Mitigating GPS Spoofing Attacks in Unmanned Aerial Vehiclesarticle