## 3 The Proposed SP-IoUAV Model
### Intrusion Detection System (IDS)
#### 3.3.2 Intrusion Detection Engine
To enhance security and access control, the center implements multi-factor authentication (MFA) requiring username, password, and facial recognition for authorized personnel to access the system. This multi-layered security approach ensures that only authorized individuals access critical data and functionalities, safeguarding the UAV network's integrity and sensitive information. With its real-time situational awareness, intelligent decision-making capabilities, and robust security measures, the smart city control center plays a pivotal role in enabling safe, efficient, and secure UAV operations in the smart city environment. Its centralized management and control contribute to the overall success of our proposed model, ensuring privacy-preserving, secure, and resilient UAV operations. In the next section, we present \"Experimental Findings and Analysis\", showcasing results and performance evaluation. ## 4 Experimental Findings and Analysis

In this section, we present a comprehensive evaluation of our proposed model's performance. This section is divided into five subsections, namely, \"System Requirements\", where we outline the hardware and software configurations used for experimentation, \"Dataset Description\", which provides insights into the dataset employed in the evaluation, \"Simulation Setup\", where we presents simulation parameters used in this research work,\"Comparative Analysis\", where we compare the results of our proposed model with existing intrusion detection systems, and \"Security Analysis of the Proposed Model\", where we assess the model's robustness against various security threats. The experimental findings and analysis aim to shed light on the effectiveness and reliability of our proposed intrusion detection system within a smart-city environment. ### System Requirements

The system requirements subsection details the essential hardware and software prerequisites for the effective implementation and operation of our system. Meeting these specific requirements, as presented in Table 3, is crucial to ensure the successful installation and smooth execution of the system. ### Dataset Description

Given the absence of a standardized intrusion dataset specifically tailored to the UAV domain, our proposed model utilized the state-of-the-art Canadian Institute of Cybersecurity dataset, CICIDS2017 [36], for both training and testing. Specifically, 80% of the dataset was allocated for training, allowing the model to learn intricate patterns and behaviors. The remaining 20% was reserved for testing, enabling the assessment of the model's performance on previously unseen data, as illustrated in \[C=\frac{1}{n-1}\sum_{i=1}^{n}(x_{i}-\bar{x})(x_{i}-\bar{x})^{T} \tag{10}\]7

This dataset comprehensively covers various normal and diverse contemporary attacks, such as Brute Force FTP, Brute Force SSH, DoS, Web Attack, Infiltration, Botnet, and DDoS. The decision to select CICIDS2017 over other options was driven by its inclusion of complex and up-to-date network attack types not found in alternative datasets. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Classes** & **Train** & **Test** \\ \hline Web\_Attack & 1772 & 443 \\ PortScan & 126,160 & 31,540 \\ Infiltration & 29 & 7 \\ DoS & 310,268 & 77,567 \\ Brute\_Force & 11,004 & 2751 \\ Botnet & 1552 & 388 \\ Normal & 1,808,288 & 452,072 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training and Testing Data Distribution for CIC-IDS2017.