## 2 Related Works
This system utilizes RL and RBFNN as agents, representing the DL component of the technique. It achieved high accuracy in identifying attacks even with an increased number of drones, outperforming state-of-the-art techniques. The BIIR approach demonstrated resilience against various known threats in real-world IoD application scenarios. However, it is important to note that our proposed SP-IoUAV model complements the BIIR approach by addressing additional security and privacy challenges in the Internet of Unmanned Aerial Vehicles. The authors of [28] proposed an advanced IDS for UAV swarms to detect in-flight anomalies and network attacks, addressing security challenges in UAV technology. The IDS offers wider attack coverage, privacy protection, and hardware independence. However, there is a potential gap in evaluating the system's real efficiency and impact in practical scenarios on hardware components. Additionally, further exploration of using blockchain for enhanced privacy and testing the IDS on a swarm of UAVs could be considered. The authors of [29] introduced a collaborative intrusion detection algorithm, utilizing CGAN-LSTM with blockchain-empowered distributed federation learning. This algorithm achieves exceptional accuracy exceeding 95% and outperforms other methods. However, to achieve even better performance, it is advisable to explore an ensemble of secure and privacy-preserving approaches. The authors of [30] presented an intelligent intrusion detection framework empowered by mobile edge computing technology for detecting and predicting diverse attacks in the UAV network. The proposed optimized random forest model, integrated into dedicated UAV-MEC servers, demonstrated efficient attack detection in various UAV network zones. However, since the framework relies on a single DNN algorithm, a combination of multiple secure and privacy-preserving approaches could potentially improve the accuracy of attack detection even further. The authors of [31] proposed a novel IDS framework based on federated continuous learning with stacked BLS learning systems. They employ DTN to enhance attack detection by decentralizing the learning process. The framework addresses data silos, ensures privacy and security, and achieves high accuracy and training efficiency. However, combining this approach with other secure and privacy-preserving techniques could further enhance performance. The authors of [32] introduced an optimized hierarchical anomaly-based intrusion detection system specialized in identifying and alerting lethal attacks in military operations within Internet of Drones networks. By utilizing an optimized hyperparameters algorithm and randomized search cross-validation, an efficient random forest classifier was designed as the baseline algorithm for the IDS. The simulation results confirmed the superiority of the proposed model, which outperformed selected optimized models based on essential performance metrics. However, the reliance on a single classifier could be further improved by exploring a combination of secure and privacy-preserving approaches for enhanced attack detection accuracy. The authors of [33] proposed a supervised ML approach for detecting Sybil attacks in FANETs-based IoFT. While achieving a high classification accuracy of over 91%, the study lacks clarity in the analysis and does not fully demonstrate the model's potential in handling diverse attacks in UAV networks. A more comprehensive evaluation of the proposed model under varied attack scenarios is needed for real-world applicability. Likewise, FANETs have been utilized in UAV networks to optimize communication using the fisheye state routing (FSR) protocol [34]. The authors of [35] proposed an innovative solution focused on preserving the privacy of lane images. Their approach combines ELA, ANN, and a U-Net model for real-time lane detection. The final step employs proxy re-encryption with RSA and ECC algorithms to ensure image integrity. However, the approach primarily addresses image security in rural lane contexts.