通过机器学习自动识别实用量子密钥分发系统中的缺陷和攻击

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jiaxin Xu, Xiao Ma, Jingyang Liu, Chunhui Zhang, Hongwei Li, Xingyu Zhou, Qin Wang
{"title":"通过机器学习自动识别实用量子密钥分发系统中的缺陷和攻击","authors":"Jiaxin Xu, Xiao Ma, Jingyang Liu, Chunhui Zhang, Hongwei Li, Xingyu Zhou, Qin Wang","doi":"10.1007/s11432-023-3988-x","DOIUrl":null,"url":null,"abstract":"<p>The realistic security of quantum key distribution (QKD) systems is currently a hot research topic in the field of quantum communications. There are always defects in practical devices, and eavesdroppers can make use of the security risk points of various devices to obtain key information. To date, current types of security analysis tend to analyze each security risk point individually, thereby posing great challenges for the overall security evaluation of QKD systems. In this paper, for the first time, we employ machine learning algorithms to identify the defects of different devices and certain attacks in real time, with an accuracy of 98%. It provides a novel solution for the practical security evaluation of QKD systems, thereby addressing the bottleneck problem of multiple risk points being difficult to address simultaneously in QKD systems, thus paving the way for the future large-scale application of quantum communication networks.</p>","PeriodicalId":21618,"journal":{"name":"Science China Information Sciences","volume":"2010 1","pages":""},"PeriodicalIF":7.3000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatically identifying imperfections and attacks in practical quantum key distribution systems via machine learning\",\"authors\":\"Jiaxin Xu, Xiao Ma, Jingyang Liu, Chunhui Zhang, Hongwei Li, Xingyu Zhou, Qin Wang\",\"doi\":\"10.1007/s11432-023-3988-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The realistic security of quantum key distribution (QKD) systems is currently a hot research topic in the field of quantum communications. There are always defects in practical devices, and eavesdroppers can make use of the security risk points of various devices to obtain key information. To date, current types of security analysis tend to analyze each security risk point individually, thereby posing great challenges for the overall security evaluation of QKD systems. In this paper, for the first time, we employ machine learning algorithms to identify the defects of different devices and certain attacks in real time, with an accuracy of 98%. It provides a novel solution for the practical security evaluation of QKD systems, thereby addressing the bottleneck problem of multiple risk points being difficult to address simultaneously in QKD systems, thus paving the way for the future large-scale application of quantum communication networks.</p>\",\"PeriodicalId\":21618,\"journal\":{\"name\":\"Science China Information Sciences\",\"volume\":\"2010 1\",\"pages\":\"\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science China Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11432-023-3988-x\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science China Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11432-023-3988-x","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

量子密钥分发(QKD)系统的现实安全性是目前量子通信领域的热门研究课题。实际设备总是存在缺陷,窃听者可以利用各种设备的安全风险点获取密钥信息。迄今为止,现有类型的安全分析倾向于单独分析每个安全风险点,从而给 QKD 系统的整体安全评估带来了巨大挑战。本文首次采用机器学习算法实时识别不同设备的缺陷和某些攻击,准确率高达 98%。这为 QKD 系统的实际安全性评估提供了一种新颖的解决方案,从而解决了 QKD 系统中多个风险点难以同时解决的瓶颈问题,为量子通信网络未来的大规模应用铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatically identifying imperfections and attacks in practical quantum key distribution systems via machine learning

The realistic security of quantum key distribution (QKD) systems is currently a hot research topic in the field of quantum communications. There are always defects in practical devices, and eavesdroppers can make use of the security risk points of various devices to obtain key information. To date, current types of security analysis tend to analyze each security risk point individually, thereby posing great challenges for the overall security evaluation of QKD systems. In this paper, for the first time, we employ machine learning algorithms to identify the defects of different devices and certain attacks in real time, with an accuracy of 98%. It provides a novel solution for the practical security evaluation of QKD systems, thereby addressing the bottleneck problem of multiple risk points being difficult to address simultaneously in QKD systems, thus paving the way for the future large-scale application of quantum communication networks.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Science China Information Sciences
Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
12.60
自引率
5.70%
发文量
224
审稿时长
8.3 months
期刊介绍: Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信