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Keystroke recognition in user authentication using ANN based RGB histogram technique

机译:使用基于ANN的RGB直方图技术进行用户身份验证中的击键识别

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In this paper, we introduce a novel authentication and discrimination system using artificial neural networks with RGB histograms. Concisely, we collect key codes and inter key times for passwords in register and login steps, colorize the keys to generate RGB histograms and train the histograms with neural networks to determine a password keystroke interval. After training determined number of histograms, our proposed system has ability to test future logins and reject the histograms which are outside of the interval. Our proposed system have successfully granted 90% of real user attempts and rejected 90% of frauds.
机译:在本文中,我们介绍了一种使用带有RGB直方图的人工神经网络的新型身份验证和鉴别系统。简而言之,我们在注册和登录步骤中收集密码的密钥代码和密钥间时间,对密钥进行着色以生成RGB直方图,并使用神经网络训练直方图以确定密码的击键间隔。在训练了确定数量的直方图之后,我们提出的系统具有测试将来的登录并拒绝间隔之外的直方图的能力。我们提出的系统已成功批准了90%的实际用户尝试,并拒绝了90%的欺诈行为。

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