## 3 The Proposed SP-IoUAV Model
### Intrusion Detection System (IDS)
#### 3.3.2 Intrusion Detection Engine
In addition to these methodologies, our model also integrates multi-factor authentication (MFA) to protect the IDS database from various types of attacks, including brute-force attacks, credential stuffing, phishing, man-in-the-middle attacks, and account takeovers. MFA adds an extra layer of security by requiring users to provide multiple authentication factors, enhancing the overall security posture of our proposed SP-IoUAV model (refer to Section 3.3.4). ## 5 Conclusions and Future Research Directions

In this research, we proposed the SP-IoUAV model, an innovative approach addressing security and privacy concerns in UAVs within the Internet of UAVs ecosystem. By incorporating a hybrid privacy-preserving mechanism that combines federated learning, differential privacy, and secure multi-party computation, our model ensures robust data protection and enhances intrusion detection accuracy. The use of deep neural networks like CNN-LSTM in our intrusion detection engine enables precise and timely threat identification, enabling real-time decision-making in dynamic environments and bolstering UAV security in smart cities. The use of multi-factor authentication (MFA) further safeguards the IDS database. Our proposed model has been evaluated using the CIC-IDS2017 dataset. The results have shown its superiority over previous approaches, such as FCL-SBL, RF-RSCV, and RBFNNs. It achieved high levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-score (99.92%). While the SP-IoUAV model demonstrates promising results, it is important to acknowledge certain limitations. For instance, the model's computational requirements may pose challenges in resource-constrained environments. Additionally, scalability and generalizability across diverse UAV mission scenarios require further investigation. In future work, we aim to integrate blockchain technology for enhanced data security and transparency. Expanding and diversifying the experimental dataset will be pivotal in refining the SP-IoUAV model. Additionally, we will focus on real-time adaptability to dynamic network conditions, multi-modal data fusion for more effective intrusion detection, and improving energy efficiency on resource-constrained UAVs. These steps are essential for strengthening our model and advancing intelligent UAV systems to meet evolving security challenges in dynamic urban environments. Conceptualization, E.N., F.A. and W.L. ; Methodology, E.N. ; Software, E.N. and M.A.L. ; Validation, E.N., F.A. and W.L. ; formal analysis, E.N. ; investigation, W.L. and F.A. ; data curation, E.N. and K.S. ; writing-Original draft preparation, E.N. ; writing-review and Editing, E.N. and K.S. ; visualization, E.N. and M.A.L. ; Supervision, W.L. and F.A. ; project administration, F.A. and W.L. ; funding acquisition, F.A. All authors have read and agreed to the published version of the manuscript. This work was funded by the Researchers Supporting Project Number (RSPD2023R564) King Saud University, Riyadh, Saudi Arabia. Not applicable. The data supporting reported results will be made available upon request. The authors declare no conflict of interest. ## References

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