SIMECK32/64的改进差分神经密码分析

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Frontiers of Computer Science Pub Date : 2023-12-02 DOI:10.1007/s11704-023-3261-z
Liu Zhang, Jinyu Lu, Zilong Wang, Chao Li
{"title":"SIMECK32/64的改进差分神经密码分析","authors":"Liu Zhang, Jinyu Lu, Zilong Wang, Chao Li","doi":"10.1007/s11704-023-3261-z","DOIUrl":null,"url":null,"abstract":"<p>In this study, we have developed a neural network aimed at enhancing the precision of neural distinguishers, demonstrating its capability to surpass DDT-based distinguishers in certain rounds. To extend the scope of our key recovery attack to additional rounds, we have diligently focused on improving both classical differentials and neural distinguishers. Consequently, we have successfully executed practical key recovery attacks on SIMECK32/64, effectively advancing the practical attack threshold by two additional rounds, allowing us to reach up to 17 rounds.</p>","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Improved differential-neural cryptanalysis for round-reduced SIMECK32/64\",\"authors\":\"Liu Zhang, Jinyu Lu, Zilong Wang, Chao Li\",\"doi\":\"10.1007/s11704-023-3261-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In this study, we have developed a neural network aimed at enhancing the precision of neural distinguishers, demonstrating its capability to surpass DDT-based distinguishers in certain rounds. To extend the scope of our key recovery attack to additional rounds, we have diligently focused on improving both classical differentials and neural distinguishers. Consequently, we have successfully executed practical key recovery attacks on SIMECK32/64, effectively advancing the practical attack threshold by two additional rounds, allowing us to reach up to 17 rounds.</p>\",\"PeriodicalId\":12640,\"journal\":{\"name\":\"Frontiers of Computer Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2023-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers of Computer Science\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s11704-023-3261-z\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Computer Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11704-023-3261-z","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 1

摘要

在这项研究中,我们开发了一个神经网络,旨在提高神经区分器的精度,并证明了它在某些回合中超过基于ddd的区分器的能力。为了将键恢复攻击的范围扩展到更多回合,我们一直在努力改进经典微分和神经区分。因此,我们已经成功地在SIMECK32/64上执行了实际的密钥恢复攻击,有效地将实际攻击阈值提高了两轮,使我们达到了17轮。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Improved differential-neural cryptanalysis for round-reduced SIMECK32/64

In this study, we have developed a neural network aimed at enhancing the precision of neural distinguishers, demonstrating its capability to surpass DDT-based distinguishers in certain rounds. To extend the scope of our key recovery attack to additional rounds, we have diligently focused on improving both classical differentials and neural distinguishers. Consequently, we have successfully executed practical key recovery attacks on SIMECK32/64, effectively advancing the practical attack threshold by two additional rounds, allowing us to reach up to 17 rounds.

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
Frontiers of Computer Science
Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
8.60
自引率
2.40%
发文量
799
审稿时长
6-12 weeks
期刊介绍: Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.
期刊最新文献
A comprehensive survey of federated transfer learning: challenges, methods and applications DMFVAE: miRNA-disease associations prediction based on deep matrix factorization method with variational autoencoder Graph foundation model ABLkit: a Python toolkit for abductive learning SEOE: an option graph based semantically embedding method for prenatal depression detection
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1