Computer-access authentication with neural network based keystroke identity verification
Computer-access authentication with neural network based keystroke identity verification
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DOI：
10.1109/icnn.1997.611659
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发表时间：
1997-06
期刊：
影响因子：
--
通讯作者：
Daw-Tung Lin
中科院分区：
文献类型：
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作者：
Daw-Tung Lin
本文提出了一种新的应用神经网络的用户身份认证的计算机访问安全系统。击键延迟是为每个用户测量的，并形成键盘动态的模式。一个三层的反向传播神经网络，具有灵活的输入节点的数量被用来区分合法用户和冒名顶替者根据每个人的密码的模式。通过在训练过程中将收敛准则RMSE设置为较小的阈值，提高了系统验证性能。最终的系统给出了1.1%的FAR（误报率）在拒绝有效用户和零IPR（冒名顶替者通过率）在接受没有冒名顶替者。所提出的识别方法的性能优于以前的研究，具有上级性能。并讨论了一种适用于该应用的网络结构。此外，该方法的实现不需要特殊的硬件，易于与大多数计算机系统集成。
This paper presents a novel application of neural nets to user identity authentication on computer-access security system. Keystroke latency is measured for each user and forms the patterns of keyboard dynamics. A three-layered backpropagation neural network with a flexible number of input nodes was used to discriminate valid users and impostors according to each individual's password keystroke pattern. System verification performance was improved by setting convergence criteria RMSE to a smaller threshold value during training procedure. The resulting system gave an 1.1% FAR (false alarm rate) in rejecting valid users and zero IPR (impostor pass rate) in accepting no impostors. The performance of the proposed identification method is superior to that of previous studies. A suitable network structure for this application was also discussed. Furthermore, the implementation of this approach requires no special hardware and is easy to be integrated with most computer systems.