## 2 Literature Review
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Algorithm/Model** & **Features/Techniques** & **Dataset** & **Accuracy** & **Key Strengths** & **Limitations** \\ \hline [PERSON] et al. [27] & LSTM-RNN & Abnormal behavior detection & Real-world GPS data & 98\% & High reliability in real time & Limited adaptability to dynamic environments \\ \hline [PERSON] et al. [28] & PCA-CNN-LSTM & Feature extraction via PCA and a CNN for local data and LSTM for sequential data & Simulated GPS dataset & 99.49\% & Comprehensive multi-layered architecture & High computational cost \\ \hline [PERSON] et al. [29] & GA-XGBoost for optimization and classification & Genetic Algorithm for optimization and XGBoost for classification & Embedded board with sensors & 96.3\% & Shorter response time & Specific to non-hijacked drones \\ \hline [PERSON] et al. [30] & LSTM + Trajectory Prediction & Flight data prediction and predefined trajectories & Flight path dataset & 78\% & Effective trajectory-based detection ratio & Moderate detection ratio and higher time cost \\ \hline [PERSON] et al. [31] & Deep-SIM & Real-time image comparison with satellite imagery & Sat-UAV dataset & 95\% & No GPS infrastructure modification & Relies on external satellite imagery \\ \hline [PERSON] et al. [32] & KNN + SVM & Signal grouping into spoofed / authentic and nearest neighbor clustering & Test GPS signal dataset & 99.5\% & High model validity and limited generalizability \\ \hline [PERSON] et al. [33] & LSTM & Continuous authentication & Drone system dataset & 73\% & Demonstrates feasibility & Low detection accuracy \\ \hline [PERSON] et al. [34] & Self-Supervised Learning & Hybrid self-supervised and transfer learning approach & Validation dataset & 79\% & Combines multiple learning paradigms & Moderate accuracy and limited robustness \\ \hline [PERSON] et al. [35] & ANN + GA & Artificial neural networks optimized with Genetic Algorithms & Simulated environmental data & +6\% (improv.) & Improved prediction accuracy & Incremental improvement and no real-time validation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the literature on GPS spoofing detection in UAVs. Moreover, while some models achieve impressive detection accuracy, they often fall short in real-time applicability, computational efficiency, or adaptability to diverse spoofing scenarios. Many rely on specific datasets that may not reflect the complexities of real-world UAV operations, limiting their generalizability. The lack of hybrid approaches that fully leverage the strengths of both advanced neural networks and optimization algorithms points to an opportunity for innovation. To address these limitations, this paper proposes a novel hybrid approach that combines LSTM networks with GAs. The optimization capabilities of GAs, coupled with the LSTM's ability to identify complex temporal patterns, aim to create a highly adaptive system capable of detecting GPS spoofing attacks in real time and across diverse operational scenarios. This paper makes the following key contributions:  
* It proposes a novel hybrid framework integrating LSTM networks with GAs, expanding the optimization search space to include both typical and atypical hyperparameter configurations, thereby improving model accuracy, robustness, and adaptability to diverse scenarios. * It undertakes a comprehensive real-time evaluation of the proposed framework, focusing on computational efficiency, scalability, and adaptability to dynamic GPS spoofing environments, demonstrating superior performance compared to existing machine learning models. * It performs a detailed comparative analysis of the hybrid LSTM-GA approach against state-of-the-art models such as PCA-CNN-LSTM and GA-XGBoost, showcasing its effectiveness in detecting GPS spoofing attacks under varied conditions and scenarios.