Factors Affecting Synchronization Time of Tree Parity Machines in Cryptography

M. Aleksandrov, Y. Bashkov
{"title":"Factors Affecting Synchronization Time of Tree Parity Machines in Cryptography","authors":"M. Aleksandrov, Y. Bashkov","doi":"10.1109/ATIT50783.2020.9349327","DOIUrl":null,"url":null,"abstract":"This article presents experimental results of evaluating factors affecting synchronization time of tree parity machines. Tree parity machines are proposed as a modification of the symmetric encryption algorithm. One of the advantages of the method consists in using the phenomenon of mutual synchronization of neural networks to generate an identical encryption key for users without the need to transfer it. As a result, the factors influencing the synchronization time of neural networks and the level of key cryptographic strength were determined. The degree of influence factors was found out experimentally. The influence of the learning rule on timing and stability of synchronization of neural networks was also determined. As a result, it was determined that the best rule for mutual learning of neural networks is Hebb’s rule, and when the architecture of neural networks becomes more complex, the number of hidden neurons should be increased first. The tasks of further research are defined.","PeriodicalId":312916,"journal":{"name":"2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ATIT50783.2020.9349327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

This article presents experimental results of evaluating factors affecting synchronization time of tree parity machines. Tree parity machines are proposed as a modification of the symmetric encryption algorithm. One of the advantages of the method consists in using the phenomenon of mutual synchronization of neural networks to generate an identical encryption key for users without the need to transfer it. As a result, the factors influencing the synchronization time of neural networks and the level of key cryptographic strength were determined. The degree of influence factors was found out experimentally. The influence of the learning rule on timing and stability of synchronization of neural networks was also determined. As a result, it was determined that the best rule for mutual learning of neural networks is Hebb’s rule, and when the architecture of neural networks becomes more complex, the number of hidden neurons should be increased first. The tasks of further research are defined.
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
密码学中影响树奇偶校验机同步时间的因素
本文给出了树奇偶校验机同步时间影响因素的实验结果。提出了树奇偶校验机作为对称加密算法的改进。该方法的优点之一是利用神经网络的相互同步现象为用户生成相同的加密密钥,而无需传输。确定了影响神经网络同步时间和密钥加密强度水平的因素。通过实验确定了各因素的影响程度。确定了学习规则对神经网络同步时间和稳定性的影响。结果确定了神经网络互学习的最佳规则为Hebb规则,当神经网络的结构变得更复杂时,应首先增加隐藏神经元的数量。明确了进一步研究的任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Detection and Testing of Dependencies Between Input and Output Data in the Implementation of Multi-Digit Algorithms in a Parallel Computational Model Information and Encoding Theory Methods of Security Authentication and Authorization into Informationals Systems An Approach to Better Portable Graphics (BPG) Compression with Providing a Desired Quality Static Analysis of Resource Consumption in Programs Using Rewriting Rules
×
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