中文摘要：

由于计算机用户对键盘的熟悉程度、击键习惯等不尽相同，每个用户都具有自己独特的击键生物特征．对于某个用户来说，其击键特征为正常类，其他所有用户为异常类，这可以利用模式识别中的单类分类器来解决．本文设计基于支持向量数据描述（SVDD）的击键生物特征身份认证系统模型．将该方法与BP、RBF和SOM方法进行对比，证实SVDD具有较好的识别效果，它可将非法用户误接受率从28．9％降低到0．28％．最后给出一个嵌入Windows用户登录中的口令＋击键特征身份认证的实现技术．

英文摘要：

Since computer users are different at degrees of familiarity with keyboards and keystroke habits, each user has his particular keystroke characteristics. To one user, his keystroke characteristics are normal classes, and all the other users＂ are abnormal. Thus this problem can be resolved by one-class classifier. The keystroke verification based on support vector data description （SVDD） is proposed. Through experiments, SVDD is compared with BP, RBF and SOM, and the results show SVDD has better performance. It decreases impostor pass rate （IPR） from 28.9% to 0. 28%. Finally, an password ＆ keystroke characteristics identity verification system is presented.