## 2 Literature Review  
There are some existing approaches based on the artificial intelligence applied in GNSS that are discussed in articles that describe the utilization of different ML/DL models at different stages, such as the RF front-end stage, pre-correlation stage, and post-correlation stage [6; 7; 8; 9; 10; 11; 12; 13; 14]. The different ML algorithms used in the literature, such as the distance-based representational model, autoregressive integrated moving average (ARIMA), and statistical measures (mean, standard deviation, variance, median, quantile, kurtosis, skewness, etc. ), have been shown that they have several shortcomings and that they cannot be used to model multivariate time series data. Some of the traditional statistical approaches used in [15] provided information about the trends and outliers in the signal, with the help of the correlation in the data on 30% of the quartile error values. Many categories of anomalies and several ML algorithms listed in the literature (supervised, semi-supervised, and unsupervised) were used to detect the GNSS abnormalities in the signal. A very detailed description of the type of data, the categories of anomalies, and the evaluation criteria are provided in [16] in the context of Internet of Things (IOT) data, which paved the way for analyzing the suitability of the selected deep learning model. Similarly, a survey of ML models and a variety of ML-based abnormality detection schemes illustrated in [17] created an interest among readers in choosing the best model for anomaly detection. Some of the studies listed here used real- and complex-valued long short-term memory (LSTM) algorithms that predicted the next time steps based on loss values [18; 19]. A hyper approach developed based on LSTM and a one-class support vector machine-based anomaly detection method has been implemented by [PERSON] et al. [20] for detecting attacks in the network of an unbalanced dataset. The method employed has the advantage of being able to operate on high-dimensional datasets by reducing the processing time. Using this hybrid model, a high detection rate was obtained for securing the Software Defined Network (SDN) from malicious traffic. An LSTM network layer has been used to denoise and forecast the interruption in the GNSS permanent station for early warning monitoring on different time series data [21; 22]. However, the data used in this work rely on post-processing observations. A recurrent neural network (RNN)-based autoencoder network constructed by [PERSON] et al. [23] is a good choice for learning the bonds among time-correlated data. This autoencoder network could detect interference with F1-scores of 0.93, 0.90, and 0.83, respectively, under three communication scenarios: a 16-quadrature amplitude modulated (QAM) signal with superimposed quadrature phase shift keying (QPSK), a frequency modulated (FM) signal, and a clean FM signal contaminated by a direct sequence spread spectrum (DSSS). This model only considers anomaly detection in generic modulation schemes. An overview of the use of LSTM models in GNSS jamming and spoofing and their limitations is presented next. Spoofing detection is carried out with the help of a general adversarial network (GAN) by taking the cross-ambiguity function (CAF) as an acquisition output for both authentic and spoofed signals [24]. The acquisition of satellite signals needs to be performed as a function of receiver signal processing. A jamming detection method using an LSTM-based model was introduced by [25] for satellite signal reception in the presence of alpha-state noise. The pure satellite signal of interest that is not a jamming instance is used for LSTM training. Here, the myriad filter was employed to suppress the alpha-state noise. A detection rate of 98% was achieved for a JSR of 7 dB.