超越密码:多因素身份验证法实现稳健的数字安全

IF 0.9 Q4 TELECOMMUNICATIONS
Keerthan Simha.R, Raghavan H K, Akshatha Prabhu, Pallavi Joshi
{"title":"超越密码:多因素身份验证法实现稳健的数字安全","authors":"Keerthan Simha.R, Raghavan H K, Akshatha Prabhu, Pallavi Joshi","doi":"10.1002/itl2.555","DOIUrl":null,"url":null,"abstract":"Multi‐Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email‐password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.","PeriodicalId":100725,"journal":{"name":"Internet Technology Letters","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beyond passwords: A multi‐factor authentication approach for robust digital security\",\"authors\":\"Keerthan Simha.R, Raghavan H K, Akshatha Prabhu, Pallavi Joshi\",\"doi\":\"10.1002/itl2.555\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi‐Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email‐password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.\",\"PeriodicalId\":100725,\"journal\":{\"name\":\"Internet Technology Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet Technology Letters\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.1002/itl2.555\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet Technology Letters","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.1002/itl2.555","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

多因素身份验证(MFA)通过要求用户验证身份来加强数字安全。它使用各种认证方法,如在传统密码之外增加一个额外的保护层。所提出的方法引入了一种新颖的多因素身份验证系统,该系统整合了多个身份验证层,从传统电子邮件密码的两阶段图形密码开始,到使用卷积神经网络(CNN)的面部识别和快速反应(QR)代码身份验证。为了证明我们方法的鲁棒性,我们考虑了一些测试案例和一些性能指标,如延迟、准确性等。结果是假阳性率和复杂性。据观察,所提模型的成功率超过 93%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Beyond passwords: A multi‐factor authentication approach for robust digital security
Multi‐Factor Authentication (MFA) strengthens digital security by necessitating users to verify their identity. It uses various authentication methods like adding an extra layer of protection beyond conventional passwords. Proposed method introduces a novel MFA system that integrates multiple authentication layers, starting with two phase Graphical password with the traditional email‐password and progressing to facial recognition using Convolutional Neural Networks (CNN) and Quick response (QR) code authentication. To prove the robustness of our method, we are considering some test cases and few performance metrics like delay, accuracy, etc. The results are derived for False positive rates, complexity. The success rate is observed to be more than 93% for the proposed model.
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
CiteScore
3.10
自引率
0.00%
发文量
0
×
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