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基于认知的分布式数据和 Web 技术认证协议。

Cognitive Based Authentication Protocol for Distributed Data and Web Technologies.

机构信息

AGH University of Science and Technology, 30 Mickiewicza Ave., PL-30-059 Kraków, Poland.

Pedagogical University of Krakow, Podchorążych 2 Street, PL-30-084 Kraków, Poland.

出版信息

Sensors (Basel). 2021 Oct 31;21(21):7265. doi: 10.3390/s21217265.

DOI:10.3390/s21217265
PMID:34770571
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8587779/
Abstract

The objective of the verification process, besides guaranteeing security, is also to be effective and robust. This means that the login should take as little time as possible, and each time allow for a successful authentication of the authorised account. In recent years, however, online users have been experiencing more and more issues with recalling their own passwords on the spot. According to research done in 2017 by LastPass on its employees, the number of personal accounts assigned to one business user currently exceeds 191 profiles and keeps growing. Remembering these many passwords, especially to applications which are not used every week, seems to be impossible without storing them either on paper, in a password manager, or saved in a file somewhere on a PC. In this article a new verification model using a Google Street View image as well as the user's personal experience and knowledge will be presented. The purpose of this scheme is to assure secure verification by creating longer passwords as well as delivering a 'password reminder' already embedded into the login scheme.

摘要

验证过程的目标除了保证安全性外,还需要高效且稳健。这意味着登录所需的时间应尽可能短,并且每次都能成功验证授权的账户。然而,近年来,在线用户在现场回忆自己的密码时遇到了越来越多的问题。根据 LastPass 公司在 2017 年对其员工进行的研究,目前每个企业用户分配的个人账户数量超过 191 个,并在不断增加。如果不将这些密码存储在纸上、密码管理器中,或者在 PC 上的某个文件中,要记住这么多密码,尤其是对于不每周使用的应用程序,似乎是不可能的。本文提出了一种新的验证模型,使用谷歌街景图像以及用户的个人经验和知识。该方案的目的是通过创建更长的密码以及在登录方案中嵌入“密码提示”来确保安全验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/aff0efdf0c32/sensors-21-07265-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/b2a4e3843946/sensors-21-07265-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/6135dbcdb09d/sensors-21-07265-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/a754b0b3d261/sensors-21-07265-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/aff0efdf0c32/sensors-21-07265-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/b2a4e3843946/sensors-21-07265-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/6135dbcdb09d/sensors-21-07265-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/a754b0b3d261/sensors-21-07265-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b17/8587779/aff0efdf0c32/sensors-21-07265-g004.jpg

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Entropy (Basel). 2020 Dec 13;22(12):1406. doi: 10.3390/e22121406.
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Adaptive Resonance Theory: how a brain learns to consciously attend, learn, and recognize a changing world.适应谐振理论:大脑如何学会有意识地关注、学习和识别不断变化的世界。
Neural Netw. 2013 Jan;37:1-47. doi: 10.1016/j.neunet.2012.09.017. Epub 2012 Oct 4.
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Proc Natl Acad Sci U S A. 2009 Apr 7;106(14):6008-10. doi: 10.1073/pnas.0811884106. Epub 2009 Mar 23.
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