基于黑盒的量子密钥分发网络测评方法研究

戴桦; 孙歆; 毛易宁; 李一璇; 吕玉祥; 王红艳; 郁小松

doi:10.13756/j.gtxyj.2025.240165

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基于黑盒的量子密钥分发网络测评方法研究

专题：光量子芯片 | 更新时间：2025-06-18

- 基于黑盒的量子密钥分发网络测评方法研究
- A Research on Black-box Evaluation Methods for Quantum Key Distribution Networks
- 光通信研究 2025年第3期
- 作者机构：
  
  1.国网浙江省电力有限公司电力科学研究院，杭州　 310014
  2.北京邮电大学　信息光子学与光通信国家重点实验室，北京　 100876
  3.国网信产集团安徽继远软件有限公司，合肥　 230097
- 作者简介：
  
  戴桦（1985-），男，浙江杭州人。高级工程师，硕士，主要研究方向为网络安全。
  郁小松，副教授。E-mail：xiaosongyu@bupt.edu.cn
- 基金信息：
  
  国网浙江省电力有限公司科技资助项目(B311DS230009)
- DOI：10.13756/j.gtxyj.2025.240165
  中图分类号： TN918
- 收稿日期：2024-08-05，
  
  修回日期：2024-09-14，
  
  纸质出版日期：2025-06-10
- 稿件说明：
移动端阅览
戴桦，孙歆，毛易宁，等. 基于黑盒的量子密钥分发网络测评方法研究[J]. 光通信研究，2025(3): 240165.

Dai H, Sun X, Mao Y N, et al. A Research on Black-box Evaluation Methods for Quantum Key Distribution Networks[J]. Study on Optical Communications, 2025(3): 240165.
戴桦，孙歆，毛易宁，等. 基于黑盒的量子密钥分发网络测评方法研究[J]. 光通信研究，2025(3): 240165. DOI： 10.13756/j.gtxyj.2025.240165.

Dai H, Sun X, Mao Y N, et al. A Research on Black-box Evaluation Methods for Quantum Key Distribution Networks[J]. Study on Optical Communications, 2025(3): 240165. DOI： 10.13756/j.gtxyj.2025.240165.

摘要

【目的】

量子密钥分发网络（QKDN）是量子密钥分发（QKD）的规模化建设，通常部署于光纤通信基础设施网络。基于量子物理学原理，QKDN中的密钥分发过程被视为理论上的绝对安全，任何未授权的干扰或测量都会破坏量子态从而被立即检测到，确保了通信的安全性和完整性。然而，由于量子态存储技术尚未成熟，由点到点QKD扩展至多点到多点QKDN需要借助经典技术实现，新的安全威胁也随之而来。拒绝服务（DoS）攻击是经典网络中的一种常见攻击形式，被攻击者广泛利用，QKDN的应用层同样面临安全挑战。

【方法】

文章提出了一种自适应范围阈值（ART）检测方法来检测DoS攻击，通过网络流量模式的异常波动自动调整阈值，从而灵活识别和响应DoS攻击。

【结果】

仿真结果表明，基于ART的DoS攻击检测方法使检测成功率（DSR）达到80%以上。另外，从延迟时间和请求成功率两个角度衡量了QKDN的网络安全性。

【结论】

文章通过设置动态阈值识别网络潜在的安全威胁，并从多维度对网络安全性做出评估，为QKDN实际运行提供了一种有效的安全测评方法，也为未来制定量子信息网络安全防御策略奠定了基础。

Abstract

【Objective】

The Quantum Key Distribution Network (QKDN) is the large-scale implementation of Quantum Key Distribution (QKD)

which is typically deployed within optic fiber communication infrastructure networks. Based on the principles of quantum physics

the key distribution process in QKDN is considered to be theoretically absolutely secure. Any unauthorized interference or measurement will destroy the quantum state and be immediately detected

ensuring the security and integrity of communication. However

since quantum state storage technology is not yet mature

the expansion from point-to-point QKD to multi-point-to-multi-point QKDN needs to be realized with the help of classical technologies

which also brings new security threats. Denial-of-Service (DoS) attacks are a common form of attack in classical networks

widely exploited by attackers

and the application layer of QKDN also faces security challenges.

【Methods】

The paper proposes an Adaptive Range Threshold (ART) detection method to detect DoS attacks. It automatically adjusts the threshold based on abnormal fluctuations in network traffic patterns

thereby flexibly identifying and responding to DoS attacks.

【Results】

Simulation results show that the Detection Success Rate (DSR) of QKDN is effectively improved by the ART-based DoS attack detection method

and the DSR can reach 80% by setting reasonable parameters. Additionally

the network security of the QKDN is measured from two perspectives: delay time and request success rate.

【Conclusion】

The paper identifies potential network security threats by setting dynamic thresholds and evaluates network security from multiple dimensions. This provides an effective security assessment method for the actual operation of QKDN and lays the foundation for future development of security defense strategies for quantum information networks.

关键词

Keywords

references

Sharma A , Kumar A . A Survey on Quantum Key Distribution [C ] // 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) . Ghaziabad, India : IEEE , 2019 : 8977649 .

Kuppa A , Grzonkowski S , Asghar M R , et al . Black Box Attacks on Deep Anomaly Detectors [C ] // Proceedings of the 14th International Conference on Availability, Reliability and Security . Canterbury, CA, UK : ACM , 2019 : 1 - 10 .

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Wu Z , Li W , Liu L , et al . Low-rate DoS Attacks, Detection, Defense, and Challenges: a Survey [J ] . IEEE Access , 2020 , 8 : 43920 - 43943 .

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Latah M , Toker L . Minimizing False Positive Rate for DoS Attack Detection: a Hybrid SDN-based Approach [J ] . ICT Express , 2020 , 6 ( 2 ): 125 - 127 .

Ali T E , Chong Y W , Manickam S . Machine Learning Techniques to Detect a DDoS Attack in SDN: a Systematic Review [J ] . Applied Sciences , 2023 , 13 ( 5 ): 3183 .

靳安然 , 李鹤 . 混合连续—离散变量量子密钥分发 [J ] . 光通信研究 , 2023 ( 3 ): 24 - 31 .

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