基于主成分分析的GNSS欺骗检测最优特征设计

IF 4.4 3区 计算机科学 Q2 TELECOMMUNICATIONS
Shuai Zhang;Chao Sun;Ying Xu;Lu Bai;Wenquan Feng;Xin Wen;Yingzhe He;Nanzhu Liu
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引用次数: 0

摘要

全球导航卫星系统(GNSS)欺骗攻击对现有5G和未来6G网络构成严重威胁,因为它们严重依赖GNSS提供的高精度时间同步信息。基于信号特征的欺骗检测方法利用早、晚相关器提取信号特征,有效地识别欺骗信号。然而,使用多个特征作为机器学习(ML)输入可能会导致维度的诅咒。针对这一问题,本研究提出了一种最优特征构建方法。该方法使用主成分分析(PCA)生成基本特征,并使用基于逻辑回归(LR)的方法将这些生成的特征构建为一个特征,即主成分回归(PCR)。然后,该研究使用一维卷积神经网络(1D CNN)对PCR方法与常规复合特征进行比较和评估。结果表明,聚合酶链反应始终优于传统特征,表明更高的欺骗检测效率和保护关键通信基础设施免受欺骗攻击的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Principal Component Analysis-Based Optimal Feature Design for GNSS Spoofing Detection
Global navigation satellite system (GNSS) spoofing attacks pose a serious threat to existing 5G and future 6G networks, as they rely heavily on the high-precision time synchronization information provided by GNSS. Signal feature-based spoofing detection methods effectively identify spoofing signals using early and late correlators to extract signal features. However, using multiple features as machine learning (ML) inputs can lead to the curse of dimensionality. To address this, the study proposes an optimal feature construction method. This method uses principal component analysis (PCA) to generate essential features and a logistic regression (LR)-based approach to construct these generated features into one feature, i.e. principal component regression (PCR). The study then employs a one-dimensional convolutional neural network (1D CNN) to compare and evaluate the PCR method against conventional composite features. The results show that the PCR consistently outperforms conventional features, indicating higher spoofing detection effectiveness and the potential to safeguard critical communication infrastructures against spoofing attacks.
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来源期刊
IEEE Communications Letters
IEEE Communications Letters 工程技术-电信学
CiteScore
8.10
自引率
7.30%
发文量
590
审稿时长
2.8 months
期刊介绍: The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.
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