## 3 The Proposed SP-IoUAV Model
### Intrusion Detection System (IDS)
#### 3.3.2 Intrusion Detection Engine
This combination enables our model to detect and classify intrusions with high accuracy while minimizing false positives and false negatives. Furthermore, data preprocessing techniques such as data normalization, feature extraction, and data transformation improve the model's ability to handle complex and dynamic UAV network data, contributing to its superior F-Score performance. Our proposed SP-IoUAV model outperformed previous approaches in terms of accuracy, precision, recall, and F-Score in this comparative analysis. Its exceptional performance was enabled by the integration of CNN-LSTM deep learning algorithms, data preprocessing, and a secure ensemble of federated learning, differential privacy, and secure multi-party computation. The real-time decision-making capability enhanced threat identification and response in dynamic environments. Overall, our SP-IoUAV model proved to be a robust and efficient solution for securing UAV-based applications in smart cities and other industries. Figure 3.0 F-score of our proposed SP-IoUAV Model. ### Security Analysis of the Proposed SP-IoUAV Model

In the security analysis of the SP-IoUAV model, we thoroughly evaluate its ability to counter a range of intrusions that could jeopardize UAV operations in a smart city. Our model effectively combats the following intrusions using advanced methodologies:

* **Distributed Denial of Service (DDoS) Attacks:** DDoS attacks pose a significant threat to UAV networks, aiming to disrupt services by flooding them with a high volume of malicious traffic. To counter this, our proposed model incorporates a sophisticated \"intrusion Detection Engine\". Within this engine, the \"Anomaly Detection and Classification\" stage employs advanced machine learning algorithms, including the powerful CNN-LSTM. This stage plays a pivotal role in recognizing and classifying various anomalies, including DDoS attacks. It excels in identifying abnormal patterns within network traffic, making it a crucial component in safeguarding the UAV network within the dynamic environment of a smart city (refer to Section 3.3.2). * **Man-in-the-Middle (MitM) Attacks:** MitM attacks attempt to intercept communication between UAVs and the smart-city infrastructure. Our model incorporates an \"Intrusion Detection Engine\" that actively monitors the network for any suspicious activities, providing an essential layer of defense against unauthorized access and ensuring ensuring data security (refer to Section 3.3.2). * **Code Injection:** In the scenarios involving code injection, where malicious code is inserted into the UAV system to compromise its operations, the SP-IoUAV model employs a multi-step approach within the intrusion detection engine. This begins with robust preprocessing techniques, including crucial sub-steps like feature extraction, all designed to enhance the system's ability to detect and prevent code injection attempts. Principal component analysis (PCA) plays a pivotal role in this process, enabling efficient feature extraction. These combined efforts ensure the integrity and seamless operation of the UAV system, even in the face of potential code injection threats (refer to Section 3.3.2). * **Eavesdropping:** Eavesdropping involves unauthorized interception of data transmission. Our model utilizes secure communication channels and encryption techniques, along with t-distributed stochastic neighbor embedding (t-SNE) for data transformation, to safeguard data privacy and prevent eavesdropping attacks (refer to Section 3.3.2). * **Spoofing:** Spoofing attacks involve impersonating legitimate UAVs or smart-city devices to gain unauthorized access. The SP-IoUAV model employs device authentication and verification mechanisms, along with min-max scaling for data normalization, to detect and thwart spoofing attempts (refer to Section 3.3.2).