Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Abstract

Unmanned Aerial Vehicles (UAVs) are an emerging technology in the 5G and beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the open-source simulator can use commercial software-defined radio tools to generate fake GPS signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations at the same time. In this paper, we propose a novel deep ensemble learning-based, mobile network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between base stations and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multi-layer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

“…This method firstly solves a sensing node access sequence by the conventional TSP method, and then it adjusts the path segment between two nodes with overlapping communication ranges and determines a new path point through rotation, optimization, and smoothing, thus shortening the flight distance. Reference [30] determines the path point at the communication range boundary of the sensing node, shortens the flight distance, and then further optimizes the data collection path by using the heuristic algorithm of point replacement and line replacement. Reference [31] firstly divides the area where sensing nodes are deployed into several unit partitions, collects the data of all sensing nodes in each unit partition, and proposes a path optimization algorithm combining the genetic algorithm and ant colony optimization algorithm to traverse the unit partition.…”

A Survey on Authentication in Satellite Internet

“…For example, the scheme in [58] utilizes CNN to extract unique fingerprints from signals received from IRIDIUM LEO satellites and authenticate satellite transmitters. Also, the scheme in [95] leverages the potential of deep ensemble methods and the statistical features of path losses between UAVs and base stations to detect GPS spoofing for cellular-connected UAVs. Owing to the benefits of PLA and deep learning, we expect that deep learning-based PLA for satellites is a promising research direction.…”

Relative Localization and Circumnavigation of a UGV0 Based on Mixed Measurements of Multi-UAVs by Employing Intelligent Sensors

“…Multi-UAV RL and navigation not only play an increasingly crucial role in civil and military fields [1,2], but have also achieved notable successes in commerce, agriculture, and medical rescue [3][4][5]. Among them, UAV positioning technology [6] is a core element ensuring its safe and efficient operation.…”