Continual Retraining of Keystroke Dynamics Based Authenticator
Continual Retraining of Keystroke Dynamics Based Authenticator
复制标题
基于击键动力学的认证器的持续再训练
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Sungzoon Cho
中科院分区：
文献类型：
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作者：
Pilsung Kang;Seongseob Hwang;Sungzoon Cho
基于按键动力学的身份验证（KDA）根据键入模式验证用户。在注册过程中，提供了一些键入模式，然后将其用于训练分类器。用户的打字样式预计不会更改。但是，有时确实会改变，从而导致高度错误的拒绝。为了实现更好的身份验证性能，我们建议通过更新培训数据集不断地重新训练分类器，并通过最新的登录模式进行登录模式。有两种更新它的方法。移动窗口使用固定数量的最新模式，而“生长”窗口使用所有新模式以及原始注册模式。我们将提出的方法应用于涉及21个用户的真实数据集。实验结果表明，移动窗口和生长窗口方法的表现都优于固定窗口方法，这不会重新训练分类器。
Keystroke dynamics based authentication (KDA) verifies a user based on the typing pattern. During enroll, a few typing patterns are provided, which are then used to train a classifier. The typing style of a user is not expected to change. However, sometimes it does change, resulting in a high false reject. In order to achieve a better authentication performance, we propose to continually retrain classifiers with recent login typing patterns by updating the training data set. There are two ways to update it. The moving window uses a fixed number of most recent patterns while the growing window uses all the new patterns as well as the original enroll patterns. We applied the proposed method to the real data set involving 21 users. The experimental results show that both the moving window and the growing window approach outperform the fixed window approach, which does not retrain a classifier.