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Performance Evaluation of Password Authentication using Associative Neural Memory Models

机译:使用关联神经记忆模型的密码验证性能评估

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They are many ways of providing security to user resources. Password authentication is a very important system security procedure to secure user resources. In order to solve the problems with traditional password authentication several methods have been introduced to provide password authentication using Associative Memories like Back Propagation Neural Network (BPNN),Hopfield Neural Network(HP NN),Bidirectional Associative Memories(BAM),Brain-State-in-a Box(BSB). Later Password authentication has been provided using Context-Sensitive Associative Memory Method (CSAM). Here in this paper we proposed performance analysis of password authentication schemes using Associative memories and CSAM using graphical Images. We observe that in comparison to existing layered and associative neural network techniques for graphical images as password, the CSAM method provides better accuracy and quicker response time to registration and password changes
机译:它们是为用户资源提供安全性的多种方法。密码验证是确保用户资源安全的非常重要的系统安全过程。为了解决传统密码身份验证的问题,已经介绍了几种使用关联存储器提供密码身份验证的方法,例如反向传播神经网络(BPNN),霍普菲尔德神经网络(HP NN),双向关联存储器(BAM),脑状态-内置盒(BSB)。后来,使用上下文相关联想存储方法(CSAM)提供了密码身份验证。在本文中,我们提出了使用关联存储器的密码认证方案和使用图形图像的CSAM的性能分析。我们观察到,与现有的将图形图像用作密码的分层和关联神经网络技术相比,CSAM方法提供了更高的准确性,并且对注册和密码更改的响应时间更快。

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