摘要
连续变量量子密钥分发(CVQKD)具有理论上的无条件安全性,这种安全性的前提假设条件是认为发送方和接收方的物理设备是完美运行的、安全可靠的。然而,在实际CVQKD系统中,窃听者可以从信源、信道以及检测端三个方面,针对实际设备存在的物理缺陷发动攻击,导致系统的实际安全性受到破坏。虽然目前针对部分实际量子攻击已有了相应的防御策略,但每种策略仅能防御对应的攻击类型,缺乏有效针对大多数攻击的通用型防御策略。本文将机器学习技术与CVQKD攻击检测相结合,提出基于K近邻(KNN)算法的CVQKD实际攻击检测方案。该方案对CVQKD系统的光脉冲进行特征提取,学习训练生成KNN预测模型,最终将该模型部署在CVQKD系统的接收端,用来检测实际量子攻击。仿真结果表明,该攻击检测方案能有效检测出针对CVQKD的多种典型量子攻击,且查准率和查全率均高于98%。
Continuous-variable quantum key distribution(CVQKD)has theoretical unconditional security,which is based on the assumption that the physical devices at the sender and receiver operate perfectly and are secure and reliable.However,in practical CVQKD systems,the eavesdropper can launch attacks from three aspects—source,channel,and detection end—by exploiting the physical flaws inherent in actual devices,thereby compromising the practical security of the system.Although corresponding defense strategies have been developed for some practical quantum attacks,each strategy can only defend against specific attack types,lacking a universal defense approach effective against most attacks.By combining machine learning techniques with attacks detection in the CVQKD,we propose a practical attacks detection scheme in this work based on the K-nearest neighbors(KNN)algorithm.This scheme extracts features from the optical pulses of the CVQKD system,trains a KNN prediction model through learning,and ultimately deploys the model at the receiver end of the CVQKD system to detect practical quantum attacks.Simulation results demonstrate that the proposed attack detection scheme can effectively identify various typical quantum attacks targeting CVQKD,with both precision and recall rates exceeding 98%.
作者
刘潺
黄磊
王铮
朱凌瑾
LIU Chan;HUANG Lei;WANG Zheng;ZHU Lingjin(School of Electronic Science and Engineering,Hunan University of Information Technology,Changsha 410151,China;College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China;Department of Information Resource,Taihe Hospital,Shiyan 442012,China;Hunan Institute of Metrology and Test,Changsha 410014,China)
出处
《量子电子学报》
北大核心
2025年第6期818-828,共11页
Chinese Journal of Quantum Electronics
基金
湖南省重点研发计划(2023GK2021,2023GK2054)。
关键词
量子信息
连续变量量子密钥分发
实际安全性
攻击检测
机器学习
quantum information
continuous-variable quantum key distribution
practical security
attacks detection
machine learning
