发表机构
IIT Mandi; DRDO（曼迪印度理工学院; 国防研究与发展组织）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对BB84 QKD系统的隐蔽攻击问题，提出基于时间QBER的机器学习框架，提取63种物理驱动时间特征，XGBoost分类器在多攻击检测中表现优异，大幅降低了误报率。
AI 中文摘要
传统BB84量子密钥分发（QKD）系统依靠固定的11%量子比特误码率（QBER）阈值来检测窃听，但隐蔽攻击可保持在该阈值以下，同时仍会破坏信道安全。本文提出一种基于时间QBER的机器学习框架，用于检测和分类BB84 QKD系统中的窃听攻击。该框架不依赖会话级平均QBER，而是提取63种物理驱动的时间特征，涵盖突发行为、时间不稳定性、基依赖不对称性及QBER损失相互作用。在噪声和有损条件下，针对7种窃听攻击和正常信道场景，对随机森林（Random Forest）、XGBoost和带径向基函数核的支持向量机（SVM-RBF）分类器进行评估。10次独立运行的平均结果显示，XGBoost性能最佳，准确率为88.01%（标准差0.47%），宏F1分数为0.8803；SVM-RBF表现相当，验证了所提特征的鲁棒性。与传统监控对比，作为攻击-正常二元检测器评估时，固定11% QBER阈值仅达25.82%准确率，误报率（FNR）为0.8477，而所提框架将FNR降至0.0198，大幅提升了对规避阈值监控的隐蔽攻击的检测能力。基于SHapley加性解释（SHAP）的可解释性分析表明，物理驱动的时间特征和信道衍生特征对识别窃听策略具有高度判别性。这些结果证明，基于时间QBER的机器学习为BB84 QKD系统中的多攻击安全监控提供了准确、可解释且实用的框架。
英文摘要
Conventional BB84 Quantum Key Distribution (QKD) systems rely on a fixed 11% Quantum Bit Error Rate (QBER) threshold to detect eavesdropping. However, stealthy attacks can remain below this threshold while still compromising channel security. This paper proposes a temporal QBER based machine learning framework for detecting and classifying eavesdropping attacks in BB84 QKD systems. Rather than relying on average session level QBER, the framework extracts 63 physics-informed temporal features capturing burst behavior, temporal instability, basis dependent asymmetry, and QBER loss interactions. Random Forest, XGBoost, and Support Vector Machine with a Radial Basis Function kernel (SVM-RBF) classifiers are evaluated on seven eavesdropping attacks and a normal channel scenario under noisy and lossy conditions. Averaged over ten independent runs, XGBoost achieves the best performance with 88.01% (0.47%) accuracy and a macro F1 score of 0.8803, while SVM-RBF performs comparably, confirming the robustness of the proposed features. Evaluated as a binary attack-versus-normal detector for comparison with conventional monitoring, a fixed 11% QBER threshold achieves only 25.82% accuracy with a False Negative Rate (FNR) of 0.8477, whereas the proposed framework reduces the FNR to 0.0198, substantially improving detection of stealthy attacks that evade threshold-based monitoring. SHapley Additive exPlanations based (SHAP) explainability shows that physics-informed temporal and channel derived features are highly discriminative for identifying eavesdropping strategies. These results demonstrate that temporal QBER driven machine learning provides an accurate, explainable, and practical framework for multi attack security monitoring in BB84 QKD systems.
