Shuai Zhang;Chao Sun;Ying Xu;Lu Bai;Wenquan Feng;Xin Wen;Yingzhe He;Nanzhu Liu
{"title":"基于主成分分析的GNSS欺骗检测最优特征设计","authors":"Shuai Zhang;Chao Sun;Ying Xu;Lu Bai;Wenquan Feng;Xin Wen;Yingzhe He;Nanzhu Liu","doi":"10.1109/LCOMM.2025.3557034","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13197,"journal":{"name":"IEEE Communications Letters","volume":"29 7","pages":"1505-1509"},"PeriodicalIF":4.4000,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Principal Component Analysis-Based Optimal Feature Design for GNSS Spoofing Detection\",\"authors\":\"Shuai Zhang;Chao Sun;Ying Xu;Lu Bai;Wenquan Feng;Xin Wen;Yingzhe He;Nanzhu Liu\",\"doi\":\"10.1109/LCOMM.2025.3557034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":13197,\"journal\":{\"name\":\"IEEE Communications Letters\",\"volume\":\"29 7\",\"pages\":\"1505-1509\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2025-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Communications Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10971402/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Communications Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10971402/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
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.
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.