AI 中文总结
研究量子身份认证中协议阶段的侧信道分析漏洞，通过量子通信测试平台实验，收集光子到达时间和光功率数据，设计特征并训练机器学习模型分类协议阶段，结果显示推断可行且准确率高，揭示漏洞并强调抗攻击稳健设计的必要性。
AI 中文摘要
量子网络可实现分布式计算与传感，量子身份认证至关重要，否则恶意中继器可能引发中间人攻击。此前工作未探索物理层侧信道分析。若攻击者推断协议阶段，可避开认证量子比特并提取数据量子比特。为此，使用量子通信测试平台进行实验研究。用分束器获取部分光信号，评估30%或10%信号被转移的两种采样设置，收集光子到达时间和光功率数据，提取并设计特征，训练机器学习模型基于侧信道观测对协议阶段进行分类。结果表明协议阶段推断可行且准确率高，30%采样时F1分数达97%，10%采样时F1分数达94%。这些发现揭示了一个被忽视的漏洞，凸显了针对侧信道推断攻击进行稳健设计的必要性。
英文摘要
Quantum networks can enable distributed computing and sensing. To realize these capabilities securely, quantum identity authentication is essential. Without authentication at the quantum layer, malicious repeaters may retain entanglement instead of performing swapping, enabling man-in-the-middle attacks (MitM) between communicating parties. Authentication mitigates this threat by embedding authentication qubits within data qubits at positions and bases based on a secret key shared a priori. While prior work analyzes security and MitM detection guarantees, physical layer side channel analysis remains unexplored. If an attacker infers protocol stages, it can avoid authentication qubits and extract data qubits, rendering authentication ineffective. To this end, we carry out experimental studies using a quantum communication testbed. A beam splitter is used to tap a portion of the optical signal, allowing the observer to collect side channel data without disrupting the quantum state. We evaluate two sampling settings, where 30% or 10% of the signal is diverted. The collected side channel data includes photon arrival timing and optical power data obtained using a single-photon detector and a power meter. Using this dataset, we extract and engineer features that capture both timing dynamics and signal intensity variations. We then train machine learning models to classify protocol stages based solely on side channel observations. Our results show that protocol-stage inference is feasible with high accuracy, reaching 98% (F1-score 97%) at 30% sampling and 96% (F1-score 94%) at 10% sampling. These findings reveal an overlooked vulnerability and highlight the need for robust designs against side channel inference attacks.
