## 2 Related Works
In contrast, our research takes a more comprehensive approach, encompassing security and privacy concerns across the entire UAV ecosystem. This includes a significant emphasis on robust intrusion detection mechanisms, crucial for safeguarding operations against a range of potential threats. In the literature, many authors have provided different methods for intrusion detection and prevention in the Internet of UAVs. However, it is crucial to acknowledge that this progression is not without its challenges. Table 1 illustrates the methods, limitations, and advantages of each research work cited in the literature survey. One significant limitation in the existing literature revolves around the security and privacy concerns associated with UAV operations. As UAVs become increasingly integrated into urban environments, safeguarding data transmission and preventing unauthorized access have emerged as critical issues. In response to these challenges, our proposed SP-IoUAV model leverages a novel privacy-preserving mechanism, combining federated learning, differential privacy, and secure multi-party computation. This approach not only enhances the detection and prevention of sensitive data breaches but also addresses the broader spectrum of security and privacy concerns within the UAV ecosystem. By mitigating these limitations, our model seeks to provide a robust framework for secure and privacy-preserving UAV operations within smart cities. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Methods** & **Limitations** & **Advantages** \\ \hline \multirow{2}{*}{[17]} & Hybrid ML technique of Logistic Regression and Random Forest & Weak security measures & High accuracy (98.58\%) \\ \cline{2-4}  & Enhanced K-vector perturbation and Random LNC (RLNC) & \multirow{2}{*}{Insufficiency of sensitive data security} & Explores UAV security, Decrypts locations \\ \cline{2-4}  & OC-SVM and K-Means++ Clustering & Unclear detection process & OC-SVM outperforms k-means \\ \hline \multirow{2}{*}{[24]} & Neural Network Algorithm & Unclear methodology description & High detection, low false alarms \\ \cline{2-4}  & LSTM & Single NN less effective & Effective point anomaly detection \\ \hline \multirow{2}{*}{[26]} & Hybrid ML (K-Nearest Neighbor (KNN), Naive Bayes (NB)), and blockchain & \multirow{2}{*}{Unclear methodology description} & Enhanced security, data reliability \\ \cline{2-4}  & Radial Basis Function Neural Network (BBFNN) and Blockchain & & \\ \hline \multirow{4}{*}{[27]} & Supervised Learning (LightGBM and GANS), and Unsupervised Learning (One-class classifier and Federated Learning) & Efficiency evaluation gap & Wider attack coverage, privacy \\ \cline{2-4}  & CGAN, Blockchain, and LSTM & Need of ensemble approaches & Exceptional accuracy (\textgreater{}95\%) \\ \hline \multirow{2}{*}{[30]} & Mobile Edge Computing (MEG), Optimized Random Forest (RCSV) & Need of ensemble for improved accuracy & Efficient attack detection in various zones \\ \hline \multirow{2}{*}{[31]} & Federated Learning, Deep Deterministic Policy Gradient (DDPG) & Ensemble technique needed & High accuracy, training efficiency \\ \hline \multirow{2}{*}{[32]} & Random Forest & Ensemble technique needed for improved accuracy & Superiority in identifying lethal attacks \\ \hline \multirow{2}{*}{[33]} & Supervised ML (J48, Classification via Regression, OneR and JRip) & Performance evaluation not systematic & High classification accuracy (\textgreater{}91\%) \\ \hline \multirow{2}{*}{[34]} & FSR protocol & Limited network scenarios & Efficient resource utilization, high delivery ratios \\ \hline \multirow{2}{*}{[35]} & ELA and ANN Algorithms & Only rural image security focus & Authentic assessment of satellite images \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related Works: Methods, Limitations, and Advantages.