3D Radio Map-Based GPS Spoofing Detection and Mitigation for Cellular-Connected UAVs

Abstract

With the upcoming 5G and beyond wireless communication system, cellular-connected Unmanned Aerial Vehicles (UAVs) are emerging as a new pattern to give assistance for target searching, emergency rescue, and network recovery. Such cellular-connected UAV systems highly rely on accurate and secure navigation systems, e.g. the Globe Navigation System (GPS). However, civil GPS services are unencrypted and vulnerable to spoofing attacks that can manipulate UAVs' location and abort the UAVs' mission. This paper leverage 3D radio map and machine learning methods to detect and mitigate GPS spoofing attacks for cellular-connected UAVs. Precisely, the edge UAV flight controller uses ray tracing tools deterministic channel models, and Kriging methods to construct a theoretical 3D radio map. Then the machine learning methods, such as Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), are employed to detect GPS spoofing by analyzing the UAV/base station reported Received Signal Strength (RSS) values and the theoretical radio map RSS values. Once spoofing is detected, the particle filter is applied to relocate the UAV and mitigate GPS deviation. The experiment results indicate that the Universal Kriging (UK) with exponential covariance function has the lowest standard errors for radio map construction. Moreover, the MLP achieves the highest spoofing detection accuracy with different spoofing margins because of the statistic prepossessing relieving environmental impacts, while the CNN has a comparable detection accuracy with less training time than MLP since CNN inputs are raw RSS data. Furthermore, the particle filter-based GPS spoofing mitigation can relocate the UAV to the real position within an error of 10 meters using 100 particles.

Post-Disaster Emergency Communications Enhanced by Drones and Non-Orthogonal Multiple Access: Three-Dimensional Deployment Optimization and Spectrum Allocation

“…Specifically, the application of the RNN in solving the deployment optimization problem is primarily based on their powerful modeling capabilities for sequential data. The RNN can handle variable-length sequences and capture dependencies within the sequence through its recurrent structure, making it highly effective in solving convex optimization problems with continuous variables [27][28][29]. The RNN can gradually adjust parameters by learning temporal information, approaching the optimal solution in a stepwise manner and demonstrating strong optimization performance in convex optimization problems [30].…”

Knowledge Distillation-Based GPS Spoofing Detection for Small UAV

“…The current methods can detect GPS spoofing of UAVs remotely and can recognize attacks effectively [21] and efficiently [22]. However, these methods need ground assistance to detect attacks, which cannot extend the range of UAV missions.…”