# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 2 Related Works  
As positioning and navigation services are available in nearly all devices, including the smallest ones, timely detection of spoofing attacks is crucial to prevent undesirable consequences. There are numerous methods for spoofing attacks detection. The simplest is the traditional method, which monitors signal strength. This approach is often employed for quick response to attacks and forms the foundation of many other spoofing detection methods [3]. Other widely used methods include data bit-based techniques such as tracking and analyzing National Marine Electronics Association (NMEA) messages [4], comparing the Time of Arrival (ToA) [5], and monitoring the Direction of Arrival (DoA) [6]. Signal processing techniques include power-based methods [7], antenna-based methods [8], and correlation peak monitoring [9,10]. A comprehensive overview of spoofing detection methods and countermeasures is provided in the survey paper [11]. Machine learning and deep learning approaches have recently become the most popular techniques for detecting GNSS spoofing attacks. Various studies analyze the effectiveness of different machine learning methods. The authors in [12] demonstrate that classification and regression decision tree models outperform other machine learning approaches for GPS signal classification. Similarly, the results in [13] show that support vector machines (SVMs) provide superior results for detecting spoofing attacks. However, [14] compares several machine learning methods and finds that k-nearest neighbors (KNN) outperforms SVMs. Results in [14,15], and [16] confirm that the SVM method is a reliable approach for identifying fake signals. A multi-parameter detection method proposed by [PERSON] et al. [16] significantly enhances spoofing detection compared to traditional methods. Neural Networks (NN), as a deep learning approach, are also commonly utilized, as shown in [17, 18, 19]. While recent developments have introduced a variety of spoofing detection methods, significant research is still being dedicated to exploring and developing innovative techniques. One method that remains relatively unexplored in the GNSS context is the radio frequency fingerprinting method. This method distinguishes between authentic and fake signals by analyzing unique signal characteristics to detect inconsistencies indicative of spoofing attacks. RFF techniques are widely applied in various domains, including Wi-Fi, the Internet of Things, and cellular networks, as presented in [20, 21, 22, 23, 24]. However, limited research is available on using RFF methods in the GNSS field. Investigations of different RFF approaches are presented in [25]. A review of RFF techniques for GNSS spoofing detection and a method for pre-correlation spoofing detection using RFF is presented in [26]. The proposed approach involves feature identification, extraction, data pre-processing, and the application of machine and deep learning classifiers (i.e., SVM, KNN, CNN). The presented findings indicate that combining various features within the SVM model yields the best results. In [27], support vector machines (SVMs) and logistic regression are utilized to classify radio frequency (RF) fingerprints (features), determining whether a signal is genuine or counterfeit, achieving an accuracy exceeding 90%. Similarly, in [28], an SVM is applied to three datasets for pre- and post-correlation [29] classification of authentic and fake signals. The findings indicate that classification in the pre-correlation domain yields higher accuracy (99.99%) compared to the post-correlation domain (87.72%). The reason for this difference lies in the additional filter processing in the post-correlation domain, which complicates the discrimination of fingerprints. An optimized random forest model is used in [30] for fake signal detection in the TEXBAT dataset with a classification accuracy of 99.7%. The framework proposed in [31] for spoofing detection within the post-correlation domain relies on a convolutional autoencoder.