发表机构
National Institute of Technology Rourkela; Indian Institute of Engineering Science and Technology, Shibpur（印度鲁尔基拉国家理工学院; 印度工程科学和科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对QKD光网络中继节点可信度问题，提出贝叶斯融合信任评分与Dijkstra路径选择框架，结合中心性排序，实现约8.93%更高的路径覆盖率。
AI 中文摘要
光网络中的量子密钥分发（QKD）能够提供信息论安全性，但其工作距离受限，因此需要中继节点来扩展覆盖范围。现有工作通常假设所有中继节点及其相关的密钥管理系统完全可信，忽视了软件漏洞和内部威胁带来的风险。为解决这一问题，本文提出了一种基于贝叶斯融合的模型来量化每个节点的可信度。我们为城域光网络制定了一个可靠性引导的可信中继节点（TRN）选择框架，其中每个节点被赋予一个信任分数。随后，我们将该信任分数映射为链路权重，并利用Dijkstra算法将其用于TRN选择的可靠路径计算。我们还使用结合特征向量中心性和介数中心性的综合分数对节点进行排序，以捕捉节点在网络中的拓扑重要性。在参考拓扑上的仿真结果表明，与度中心性等传统基于中心性的方法相比，在使用相同数量（约十个）TRN的情况下，所提方法实现了约8.93%更高的路径覆盖率，从而为QKD使能的光网络提供了可靠且具有弹性的TRN选择支持。
英文摘要
Quantum Key Distribution (QKD) in optical networks can provide information-theoretic security but is limited by its operability range, thereby requiring repeater nodes for extended coverage. Existing works typically assume that all repeater nodes and their associated key management systems are fully trusted, overlooking the risks posed by software exploits and insider threats. To address this, in this work, we propose a Bayesian fusion-based model to quantify the trustworthiness of each node. We have formulated a reliability-guided Trusted Repeater Node (TRN) selection framework for metro optical networks, where each node is assigned a trust score. We have subsequently mapped this trust score to link weights, which in turn has been used for reliable path computation for TRN selection, using the Dijkstra algorithm. We have also ranked the nodes using a composite score combining eigenvector and betweenness centrality, to capture their topographical relevance in the network. Simulation results on a reference topology demonstrate that the proposed method achieves approximately 8.93% higher path coverage than traditional centrality-based approaches like degree centrality, using the same number (around ten) of TRNs, thereby supporting reliable and resilient TRN selection for QKD-enabled optical networks.
Comments6 pages, 5 figures. Accepted to the IEEE ANTS 2026
