## 3 Proposed Approach: Quantum Key Distribution (QKD)
### Analysis and Verification of the Proposed Method
#### 3.3.2 Security Analysis: Resistance to Cyber Attacks
The simulation framework was built upon the Robot Operating System 2 (ROS2) integrated with the Gazebo simulator, providing a modular and customizable platform for UAV operations and security analysis (arxiv.org)  
UAV Network Configuration: A network of multiple UAVs was simulated, each equipped with the proposed encryption algorithm integrating Quantum Key Distribution (QKD), AES-128 encryption, and Lempel-Ziv-Welch (LZW) data compression. The UAVs communicated over wireless channels, emulating real-world conditions. Attack Scenarios: The simulation encompassed various cyber-attack vectors, including the following:  
- Packet Injection Attacks: Unauthorized data packets were introduced into the network to assess the system's ability to detect and handle unexpected or malicious data. - Replay Attacks: Previously captured legitimate data packets were retransmitted to evaluate the system's resilience against duplicated transmissions. - Denial-of-Service (DoS) Attacks: The network was flooded with excessive requests to test the system's capacity to maintain performance under high-load conditions. The system's performance was evaluated based on the following metrics:  
Latency: The time delay in data transmission between UAVs and the ground station. The following graph illustrates the average latency experienced by the UAV network during normal operations and under different attack scenarios (Figure 5). During normal operation, the system maintains a low average latency of approximately 50 ms, reflecting efficient data transmission with minimal processing delays. However, when subjected to a packet injection attack, the latency increases significantly to around 120 ms. This rise is primarily due to the additional processing load required to analyze and filter out unauthorized packets, which disrupts the smooth flow of legitimate data. Figure 5: System performance (average latency) under various cyber-attack scenarios. The algorithm maintains an acceptable latency, detection rate, and recovery time across different attack conditions, validating its robustness in UAV network environments. In the case of a replay attack, the latency escalates further to approximately 150 ms as the system engages in more complex verification processes to identify and discard duplicated packets, ensuring data integrity. The most pronounced impact is observed during a Denial-of-Service (DoS) attack, where the latency spikes dramatically to around 300 ms. This substantial delay indicates severe network congestion caused by the overwhelming volume of malicious traffic designed to exhaust system resources. Critically, these observations highlight the system's vulnerability to high-volume attacks, such as DoS, where the ability to maintain optimal performance diminishes despite effective detection mechanisms. While the system demonstrates resilience under moderate attack conditions, the substantial latency increases during intensive attacks suggest the need for more robust traffic management and mitigation strategies to preserve system performance under extreme stress. Detection Rate: The proportion of successfully identified malicious activities. Table 6 summarizes the detection rates of the system when subjected to various attack scenarios. The system demonstrates a consistently high detection rate across all attack scenarios, indicating robust security mechanisms capable of identifying malicious activities with remarkable accuracy. The highest detection rate is observed during packet injection attacks, where the system efficiently identifies and filters unauthorized packets. This high detection performance can be attributed to the clear signature patterns and anomalies introduced by injected packets, which are easier to flag using the system's built-in authentication and validation protocols. In contrast, a slightly lower detection rate is noted during Denial-of-Service (DoS) attacks.