## 4 The Proposed Key Distribution Framework
### A Multipath-Multi-Key Distribution Algorithm
#### 4.2.3 Selection Phase  
CP plays a pivotal role in determining the most secure and efficient routes for QKD between the source and destination nodes. The process begins with an initially empty route combination sequence table (RCST), which is then populated based on a comprehensive analysis of current network conditions, historical data, and potential security threats.  
Process Overview  
* _Data collection:_ The CP collects and analyzes real-time and historical network data, including traffic patterns, previous security breaches, and ongoing threats. The data help to predict potential vulnerabilities and optimize route selections.
* _Path enumeration:_ Utilizing algorithms like those described in Section 4.2.1, the CP enumerates all possible paths that could potentially connect \(S\) and \(D\). This enumeration considers not only the shortest paths but also alternative routes that may offer greater  
Figure 5: The process of the proposed model, considering the selection, execution, and testing phases.  
security against potential eavesdropping or quantum attacks. Techniques such as the tandem queue decomposition (TQD) policy are utilized to achieve secure and efficient packet routing by considering time-varying key availability and link capacities [41].
* _Optimization and selection:_ Each potential route combination is evaluated as presented in Algorithm 1, which assesses routes based on their security probability and operational efficiency. The algorithm prioritizes routes with the highest security metrics, ensuring compliance with quantum security standards. To provide a more comprehensive assessment of route security, metrics such as the quantum bit error rate (QBER) and key generation rates (KGR) could be incorporated into the optimization process [43].
* _Threshold evaluation:_ The security probability of each proposed distribution is compared against a predefined threshold \(\Delta_{\text{Sec}}\), which represents the minimum acceptable security level, typically set above the standard achieved by classical Dijkstra-based routing methods. This threshold should be set according to the specific security requirements of the network and can be dynamically adjusted to respond to evolving threats [44].
* _Iterative improvement:_ If the selected route combination (RCS, \(R\)) meets the security and efficiency criteria, it serves as a baseline for further optimization in subsequent iterations. This iterative process continues until no significant improvements in the route combinations are found, ensuring that the network adapts dynamically to changing conditions and requirements [45].  
MTD-Based Multi-Pathfinding  
Following the selection of an optimal RCS, the CP configures multiple paths to distribute quantum keys. This process involves:  
* _Path optimization:_ For each pair of keys, the CP utilizes feedback from previous distributions and current network data to select the most secure and efficient paths. This step is vital to maintaining high security, especially in environments with dynamic threat landscapes. To enhance this process, an adaptive routing strategy that considers key consumption and link state information is implemented [46].
* _Dynamic adaptation:_ The optimization algorithm continuously adjusts the selected paths based on real-time feedback and network conditions. This dynamic adaptation mitigates new or evolving threats, ensuring that the key distribution remains secure [45].
* _Termination:_ The algorithm terminates when all possible secure paths have been optimized or when all key pairs have been successfully assigned secure paths for distribution. Establishing clear termination criteria based on network performance metrics ensures efficient resource utilization.