## 2 Literature Review  
Numerous defense solutions have been proposed to address the security concerns surrounding GPS spoofing attacks on UAVs, showcasing a variety of algorithms designed to mitigate these risks. Beyond machine learning methods, several traditional cybersecurity defenses have been explored for detecting and mitigating GPS spoofing. Cryptographic authentication techniques, such as spread-spectrum modulation or signal watermarking, aim to validate genuine satellite transmissions at the protocol level [24]. Multi-antenna strategies leverage angle-of-arrival or phase-difference measurements to discriminate between authentic and spoofed signals [25]. Other techniques such as sensor fusion-based approaches integrate onboard inertial data, altimeters, or visual cues to detect discrepanciesin location or velocity [26]. Although these methods can be effective under certain conditions, they may necessitate substantial hardware modifications. They also incur higher costs or lack flexibility when confronting new spoofing tactics. Consequently, machine learning-based solutions provide a more adaptive, software-centric approach capable of recognizing subtle, evolving threat patterns without extensive alterations to existing UAV infrastructure and hardware. [PERSON] et al. introduced an abnormal behavior detection scheme utilizing recurrent neural networks (RNNs), with the LSTM-RNN emerging as the most effective model, achieving an average detection reliability of approximately 98% in real-time environments [27]. Similarly, [PERSON] et al. developed a PCA-CNN-LSTM model to disclose GPS signal spoofing in small UAVs [28]. The process included multiple steps: GPS signal dataset acquisition and pre-processing, PCA for feature extraction, a CNN for local feature extraction, and LSTM for supplementary processing and modeling. The neural network model of the suggested PCA-CNN-LSTM achieved a foremost accuracy of 99.49% in discerning GPS signal spoofing, outperforming traditional machine learning and deep learning models, such as Decision Trees, K-nearest neighbors (KNNs), logistic regression (LR), Support Vector Machines (SVMs), and many others. In another approach, [PERSON] et al. [29] proposed an advanced technique for detecting drone hijacking using a two-step training phase, which involved applying the GA to calibrate the values of training parameters and XG-Boost to refine the definitiveness of prediction outcomes (GA-XG-Boost). This method was assessed in real time with an off-the-shelf multi-core embedded board, along with an autopilot sensor board. The results demonstrated a shorter response time compared to other methods, with the capability to ascertain drone hijacking with high precision of 96.3% in non-hijacked drones. In conclusion, their method proved useful in detecting drone hijacking and can be applied to real-world applications. [PERSON] et al. [30] proposed an algorithm that involved two parts. The first part presented the use of an LSTM algorithm to predict the UAV's flight path ahead of time by collecting flight data including speed, direction, and distance from the starting point. Based on these prerequisites, they trained the LSTM algorithm to anticipate the ensuing flight trajectory. In the second part, they developed several predefined flight trajectories; a GPS spoofing attack was detected when the UAV's received positioning signal deviated from the predicted trajectory, which was inspected by the algorithm. The presented algorithm achieved a detection ratio of 78% with a time cost of 3 s.  
Complementing these efforts, [PERSON] et al. presented the Deep-SIM algorithm [31], which utilizes satellite imagery to identify spoofing attacks. The Deep-SIM model differentiates real-time camera images with historical satellite images, thus detecting GPS spoofing attacks against UAVs. The models were tested on the Sat-UAV dataset, which contained 967 image pairs. The ideal model achieved a favorable percentage of nearly 95% in detecting GPS spoofing attacks within less than 100 milliseconds.