Year: 2025
Type: article
Abstract: Unmanned 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.
Authors: Alejandro Salgado, Yezid Donoso
Institution: Universidad de Los Andes
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $539
OpenAlex ID: W4411864985