## 2 Related Works  
The Literature Survey section reviews existing research on secure and privacy-preserving intrusion detection for UAVs, providing insights for our proposed SP-IoUAV model. The authors of [17] introduced a hybrid ML approach, combining logistic regression and random forest, to classify data instances for enhanced privacy and security protection in drones. The technique demonstrated a remarkable accuracy of 98.58%. However, to ensure proper performance evaluation, the authors did not manage to clearly illustrate how their methodology can provide privacy and security protection in UAVs. The authors of [18] conducted an investigation into security challenges within UAV networks, specifically concerning eavesdropping attacks enabled by the broadcast nature of wireless channels and wide aerial coverage. They explored the application of machine learning techniques to decrypt encrypted locations derived from wireless data transmission. To counter machine-learning-based attacks, they proposed a location protection approach utilizing random linear network coding and randomized encryption keys. The research demonstrated the neural network's capability to successfully decrypt encrypted locations using conventional protection methods. However, this highlights the necessity for implementing stronger security measures to safeguard sensitive location data transmitted in UAV networks over wireless channels. The authors of [23] proposed two frameworks, one for creating test data features from wireless signals and another for generating training data to detect eavesdropping attacks in UAV-aided wireless systems. Their results showed that OC-SVM outperformed k-means in terms of stability, while k-means clustering performed better when the eavesdropper used high transmission power. However, the process of detection remained unclear. The authors of [24] introduced a supervised machine learning approach utilizing an artificial neural network to detect GPS spoofing signals. Various features, including pseudo-range, Doppler shift, and signal-to-noise ratio (SNR), were employed to classify GPS signals for detecting GPS spoofing attacks on unmanned aerial systems. The results demonstrated the high probability of detection and low probability of false alarms achieved by their proposed method. However, the details of the methodology remain unclear. The authors of [25] introduced a long short-term memory (LSTM) recurrent neural network method for UAV anomaly detection. Initially, a prediction model was formulated based on a training dataset containing normal data only, enabling the prediction of data at a subsequent time. Subsequently, by considering the prediction results during the training phase, an estimation of the prediction uncertainty was obtained. Finally, anomaly detection was accomplished by comparing the prediction value with the uncertain interval. The proposed method was validated using real UAV sensor data with point anomalies in north velocity and pneumatic lifting velocity, showing its effective ability to detect point anomalies. However, using a single neural network method may not be as effective as using a hybrid architecture, which could potentially offer improved performance in UAV anomaly detection. The authors of [26] proposed an approach that addresses data privacy in UAVs using blockchain technology. They analyzed security solutions combining machine learning and blockchain for UAV collaborative applications. The findings show that machine learning enhances UAV security, and blockchain offers decentralized security. The hybrid model of machine learning and blockchain improves data reliability. This approach can benefit diverse applications, including healthcare and industries where security and data qualityare vital. However, further research is needed to explore the full potential of blockchain technology for decentralized UAV security. The authors of [27] proposed a powerful DL-based blockchain IDS named BIIR for secure IoD environments.