Abstract:
Advances in wireless networks and the rapid development of electronic components have actively contributed to the emergence of new communication and surveillance systems ...Show MoreMetadata
Abstract:
Advances in wireless networks and the rapid development of electronic components have actively contributed to the emergence of new communication and surveillance systems known as Unmanned Aerial Systems (UASs). In such systems, unmanned aerial vehicles (UAVs) can be used as a wireless ad hoc network and thus provide a communications infrastructure for diverse military or civil applications. For more efficiency, a swarm of drones can be deployed in an area of interest (e.g. disaster areas, battlefields) by forming a flying ad hoc network (FANET) capable of communicating wirelessly with a ground control station (GCS) in a more secure manner. In this work, we particularly focus on the detection of False Data Injection (FDI) attacks in Unmanned Aerial Systems. We propose a new approach based on mobile agents to collect data and an artificial neural network model to identify injected false data. Our approach is validated using realistic datasets, provided by the University of Minnesota UAS Laboratories, and our results show that our proposal outperforms the compared approach by demonstrating higher detection rates (>94%) and lower false positive rates (<; 2.2%).
Date of Conference: 28 June 2021 - 02 July 2021
Date Added to IEEE Xplore: 09 August 2021
ISBN Information: