Nabi Imtiaz, Farooq Salma Zainab, Saeed Sunnyaha, Irtaza Syed Ali, Shehzad Khurram, Arif Mohammad, Khan Inayat, Ahmad Shafiq
National Center of GIS and Space Applications (NCGSA), Institute of Space Technology, Islamabad, Pakistan.
Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan.
PeerJ Comput Sci. 2024 Nov 11;10:e2399. doi: 10.7717/peerj-cs.2399. eCollection 2024.
Radio frequency interference disrupts services offered by Global Navigation Satellite Systems (GNSS). Spoofing is the transmission of structured interference signals intended to deceive GNSS location and timing services. The identification of spoofing is vital, especially for safety-of-life aviation services, since the receiver is unaware of counterfeit signals. Although numerous spoofing detection and mitigation techniques have been developed, spoofing attacks are becoming more sophisticated, limiting most of these methods. This study explores the application of machine learning techniques for discerning authentic signals from counterfeit ones. The investigation particularly focuses on the secure code estimation and replay (SCER) spoofing attack, one of the most challenging type of spoofing attacks, ds8 scenario of the Texas Spoofing Test Battery (TEXBAT) dataset. The proposed framework uses tracking data from delay lock loop correlators as intrinsic features to train four distinct machine learning (ML) models: logistic regression, support vector machines (SVM) classifier, K-nearest neighbors (KNN), and decision tree. The models are trained employing a random six-fold cross-validation methodology. It can be observed that both logistic regression and SVM can detect spoofing with a mean F1-score of 94%. However, logistic regression provides 165dB gain in terms of time efficiency as compared to SVM and 3 better than decision tree-based classifier. These performance metrics as well as receiver operating characteristic curve analysis make logistic regression the desirable approach for identifying SCER structured interference.
射频干扰会扰乱全球导航卫星系统（GNSS）提供的服务。欺骗是指传输结构化干扰信号，旨在欺骗GNSS的定位和定时服务。欺骗的识别至关重要，特别是对于生命安全航空服务，因为接收器无法识别伪造信号。尽管已经开发了许多欺骗检测和缓解技术，但欺骗攻击正变得越来越复杂，限制了大多数这些方法的有效性。本研究探讨了机器学习技术在辨别真实信号与伪造信号方面的应用。该调查特别关注安全码估计与重放（SCER）欺骗攻击，这是最具挑战性的欺骗攻击类型之一，使用得克萨斯欺骗测试电池（TEXBAT）数据集的ds8场景。所提出的框架使用来自延迟锁定环相关器的跟踪数据作为内在特征，来训练四种不同的机器学习（ML）模型：逻辑回归、支持向量机（SVM）分类器、K近邻（KNN）和决策树。这些模型采用随机六折交叉验证方法进行训练。可以观察到，逻辑回归和SVM都能以94%的平均F1分数检测到欺骗。然而，与SVM相比，逻辑回归在时间效率方面提供了165dB的增益，并且比基于决策树的分类器高出3dB。这些性能指标以及接收器操作特性曲线分析使得逻辑回归成为识别SCER结构化干扰的理想方法。
