Machine Learning Modeling of GPS Features with Applications to UAV Location Spoofing Detection and Classification
Machine Learning Modeling of GPS Features with Applications to UAV Location Spoofing Detection and Classification
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DOI：
10.1016/j.cose.2022.103085
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Mohammad Nayfeh;Yuchen Li;K. Shamaileh;V. Devabhaktuni;N. Kaabouch
中科院分区：
文献类型：
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作者：
Mohammad Nayfeh;Yuchen Li;K. Shamaileh;V. Devabhaktuni;N. Kaabouch
提出了一种基于机器学习（ML）的无人机全球定位系统（GPS）欺骗检测与分类方法。三个测试方案是在室外但控制设置调查静态和动态攻击。在这些场景中，会收集真实的GPS信号特征集，然后在无人机受到软件定义无线电（SDR）收发器模块发起的欺骗攻击时获得其他集。所有集合都经过标准化，分析相关性，并在训练，验证和测试不同的多类ML分类器之前根据特征重要性进行缩减。这些分类器的性能评估结果显示，检测率（DR），误检率（MDR），和误报率（FAR）分别优于92%，13%和4%，连同亚毫秒级的检测时间。因此，所提出的建模有利于准确的实时GPS欺骗检测和分类的无人机应用。
In this paper, machine learning (ML) modeling is proposed for the detection and classification of global positioning system (GPS) spoofing in unmanned aerial vehicles (UAVs). Three testing scenarios are implemented in an outdoor yet controlled setup to investigate static and dynamic attacks. In these scenarios, authentic sets of GPS signal features are collected, followed by other sets obtained while the UAV is under spoofing attacks launched with a software-defined radio (SDR) transceiver module. All sets are standardized, analyzed for correlation, and reduced according to feature importance prior to their exploitation in training, validating, and testing different multiclass ML classifiers. The resulting performance evaluation of these classifiers shows a detection rate (DR), misdetection rate (MDR), and false alarm rate (FAR) better than 92%, 13%, and 4%, respectively, together with a sub-millisecond detection time. Hence, the proposed modeling facilitates accurate real-time GPS spoofing detection and classification for UAV applications.