Gallardo Francisco, Pérez-Yuste Antonio, Konovaltsev Andriy
ETSI Sistemas de Telecomunicación, Universidad Politécnica de Madrid, 28031 Madrid, Spain.
DLR GfR mbH, 82234 Weßling, Germany.
Sensors (Basel). 2024 Dec 1;24(23):7698. doi: 10.3390/s24237698.
Spoofing attacks pose a significant security risk for organizations and systems relying on global navigation satellite systems (GNSS) for their operations. While the existing spoofing detection methods have shown some effectiveness, these can be vulnerable to certain attacks, such as secure code estimation and replay (SCER) attacks, among others.This paper analyzes the potential of satellite fingerprinting methods for GNSS spoofing detection and benchmarks their performance using real (in realistic scenarios by using GPS and Galileo signals generated and recorded in the advanced GNSS simulation facility of DLR) GNSS signals and scenarios. Our results show that our proposed fingerprinting methods can improve the detection accuracy of the existing methods and can be coupled with other techniques to enhance the overall performance of the detection systems, all based on relatively simple metrics. In this paper, we compare the performance of several fingerprinting methods, including those from the existing literature (based on signal Gaussian properties of the signal complex envelope, energy and in-phase symbol dispersion) and one proposed in this paper, based on the satellite instrumental delay. The innovation of this work is a new jamming and spoofing complementary detection technique, based on fingerprinting and machine learning, including a new fingerprinting metric (based on the satellite instrumental delay).
欺骗攻击对依赖全球导航卫星系统（GNSS）进行运营的组织和系统构成了重大安全风险。虽然现有的欺骗检测方法已显示出一定成效，但这些方法可能容易受到某些攻击，例如安全码估计和重放（SCER）攻击等。本文分析了卫星指纹识别方法在GNSS欺骗检测中的潜力，并使用真实的（在现实场景中，通过使用德国航空航天中心先进GNSS模拟设施生成和记录的GPS和伽利略信号）GNSS信号及场景对其性能进行了基准测试。我们的结果表明，我们提出的指纹识别方法能够提高现有方法的检测准确率，并且可以与其他技术相结合以提升检测系统的整体性能，所有这些均基于相对简单的指标。在本文中，我们比较了几种指纹识别方法的性能，包括现有文献中的方法（基于信号复包络的信号高斯特性、能量和同相符号离散度）以及本文提出的一种基于卫星仪器延迟的方法。这项工作的创新之处在于一种基于指纹识别和机器学习的新型干扰与欺骗互补检测技术，其中包括一种新的指纹识别指标（基于卫星仪器延迟）。
