# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
###### Abstract  
All current transportation systems (vehicles, trucks, planes, etc.) rely on the Global Positioning System (GPS) as their main navigation technology. GPS receivers collect signals from multiple satellites and are able to provide more or less accurate positioning. For civilian applications, GPS signals are sent without any encryption system. For this reason, they are vulnerable to various attacks, and the most prevalent one is known as GPS spoofing. The main consequence is the loss of position monitoring, which may increase damage risks in terms of crashes or hijacking. In this study, we focus on UAV (unmanned aerial vehicle) positioning attacks. We first review numerous techniques for detecting and mitigating GPS spoofing attacks, finding that various types of attacks may occur. In the literature, many studies have focused on only one type of attack. We believe that targeting the study of many attacks is crucial for developing efficient mitigation mechanisms. Thus, we have explored a well-known datasetcontaining authentic UAV signals along with spoofed signals (with three types of attacked signals). As a main contribution, we propose a more interpretable approach to exploit the dataset by extracting individual mission sequences, handling non-stationary features, and converting the GPS raw data into a simplified structured format. Then, we design tree-based machine learning algorithms, namely decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), for the purpose of classifying signal types and to recognize spoofing attacks. Our main findings are as follows: (a) random forest has significant capability in detecting and classifying GPS spoofing attacks, outperforming the other models. (b) We have been able to detect most types of attacks and distinguish them. G 2025254045 20252  
and battery capacity. Meanwhile, some models are equipped with complementary components, such as an inertial measurement unit (IMU), visual odometry, and optical flow sensors, to expand their functions. The growing reliance on UAVs is accompanied by significant security and privacy concerns. These systems are highly vulnerable to a wide range of cyberattacks that threaten their integrity, confidentiality, and availability. A systematic classification of cyberattacks against UAVs was presented at four different levels [3]. At the software level, the threats include malicious software and operating system attacks. The hardware level is targeted by hijacking attempts and physical collisions. The communication level faces risks such as denial-of-service (DoS) attacks and node misbehavior. Finally, at the sensor level, UAVs are susceptible to false sensor data injection and GPS jamming. These attacks can severely compromise UAV operations, leading to mission failures, data breaches, and even physical damage. Our study focuses on sensor-based vulnerabilities, specifically those threatening UAVs' navigational system, which may be leveraged into hardware-level threats. The complexity of managing UAV flights in dynamic environments, such as in cyberattack situations, requires control systems to continuously interact with multiple sensors over time for the purpose of detecting and isolating raised disturbances. This introduces nonlinearity challenges in modeling motion behavior based on control inputs. Therefore, essential security actions involve learning-based and state-estimation-based methods. In [4], the authors designed a neural network model empowered with an extended Kalman filter for the online detection and isolation of a cyberattack that affects UAVs' navigation system, specifically involving the injection of false data (FDI) into the inertial measurement unit (IMU) sensor. Ref. [5] introduced a real-time health monitoring framework to detect potential anomalies in the aircraft engine system. Their approach consists initially of applying kernel principal component analysis (KPCA) to extract highly correlated flight variables.