With the rapid evolution of interactive multimedia systems, ensuring strong security measures has become increasingly vital. Autonomous platforms, such as drones, are vulnerable to sophisticated cyber threats, including jamming and spoofing attacks. One common spoofing strategy involves manipulating Global Positioning System (GPS) signals. By broadcasting counterfeit signals, attackers can deceive drone navigation systems. To mitigate these risks, this research introduces a machine learning-based framework aimed at intelligently detecting spoofing attempts. The approach employs signal characteristics that reflect variations in jitter, shimmer, and frequency modulations, rooted in mathematical analysis. A private dataset is used for this work. This dataset, developed by our research team, is collected under varied environmental conditions, such as during daylight and in low-light settings, over multiple sessions. It categorizes signal statistics into three distinct ranges: the initial and final segments suggest spoofed signals, whereas the middle range corresponds to genuine ones. Further, using signal reception strength (SRS) values, additional data was sourced from trusted Long Range Wide Area Network (LoRaWAN) devices. This information supports the development of a singular-class support vector machine (S-CSVM) classifier for spoofing detection. For training purposes, the dataset was partitioned into two subsets: a training set (Ttrain) and a testing set (Ttest). The performance of the model is evaluated using standard metrics, including precision, recall, F-score, and overall accuracy. By utilizing all available features, the model achieves its highest scores: 99.99% precision, 99.77% recall, and 99.95% F-score. The highest accuracy of 99.22% is achieved when all features are selected, and the distance between LoRaWAN devices and the monitoring device ranges from 6 to 8 m, outperforming the results obtained through feature selection in the ablation study. A thorough evaluation further highlights how the proposed ML-based solution outperforms existing methods.

中文翻译：

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利用机器学习驱动的方法保护自主系统免受 GPS 欺骗

随着互动多媒体系统的快速发展，确保强有力的安全措施变得越来越重要。自主平台，如无人机，容易受到复杂的网络威胁，包括干扰和欺骗攻击。一种常见的欺骗策略是控全球定位系统（GPS）信号。通过广播假信号，攻击者可以欺骗无人机导航系统。为降低这些风险，本研究引入了一个基于机器学习的框架，旨在智能检测伪造尝试。该方法采用反映抖动、闪烁和频率调制变化的信号特性，基于数学分析。本工作使用私有数据集。该数据集由我们的研究团队开发，在不同环境条件下收集，如白天和低光环境，分多次采集。它将信号统计分为三个不同范围：首段和末段显示为伪造信号，中间段则对应真实信号。此外，利用信号接收强度（SRS）值，额外数据来自可信的长距离广域网（LoRaWAN）设备。这些信息支持了奇异类支持向量机（S-CSVM）分类器的开发，用于欺骗检测。为训练目的，数据集被划分为两个子集：训练集（Ttrain）和测试集（Ttest）。模型的性能通过标准指标评估，包括精度、回忆率、F 分数和整体准确率。通过充分利用所有可用功能，模型获得了最高得分：99.99%的准确率，99.77%的召回率和 99.95%的 F 评分。 当所有特征都被选中时，最高准确率为 99.22%，LoRaWAN 设备与监测设备之间的距离范围在 6 到 8 米之间，优于消融研究中通过特征选择获得的结果。全面评估进一步凸显了基于机器学习的解决方案优于现有方法。