Mouzai Mustapha, Riahla Mohamed Amine, Keziou Amor, Fouchal Hacène
LIMOSE Laboratory, University M'Hamed Bougara of Boumerdes, Boumerdes 35000, Algeria.
CNRS, LMR, Université de Reims Champagne-Ardenne, 51687 Reims, France.
Sensors (Basel). 2025 Jun 28;25(13):4045. doi: 10.3390/s25134045.
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.
目前所有的运输系统（车辆、卡车、飞机等）都依赖全球定位系统（GPS）作为其主要导航技术。GPS接收器从多颗卫星收集信号，并能够提供或多或少准确的定位。对于民用应用，GPS信号在发送时没有任何加密系统。因此，它们容易受到各种攻击，最常见的一种攻击被称为GPS欺骗。主要后果是失去位置监控，这可能会增加碰撞或劫持方面的损害风险。在本研究中，我们专注于无人机（无人驾驶飞行器）定位攻击。我们首先回顾了许多检测和减轻GPS欺骗攻击的技术，发现可能会发生各种类型的攻击。在文献中，许多研究只关注一种类型的攻击。我们认为，针对多种攻击进行研究对于开发有效的缓解机制至关重要。因此，我们探索了一个著名的数据集，其中包含真实的无人机信号以及被欺骗的信号（有三种类型的受攻击信号）。作为主要贡献，我们提出了一种更具可解释性的方法来利用该数据集，即提取单个任务序列、处理非平稳特征，并将GPS原始数据转换为简化的结构化格式。然后，我们设计基于树的机器学习算法，即决策树（DT）、随机森林（RF）和极端梯度提升（XGBoost），用于对信号类型进行分类并识别欺骗攻击。我们的主要发现如下：（a）随机森林在检测和分类GPS欺骗攻击方面具有显著能力，优于其他模型。（b）我们能够检测出大多数类型的攻击并加以区分。
