基于支持向量机的计算机键盘用户身份验真

口令认证因为简便易实现而被大多数计算机系统所采用，但容易被盗用，存在着严重的安全隐患，而利用对用户的键入特性的识别，可以大大加强口令认证的可靠性，在对国内外众多学者所做工作研究的基础上，鉴于支持向量机在进行模式识别对所具有的优良性能，提出利用支持向量机进行键入特性验真，并通过实验将其与BP，RBF，PNN和LVQ四种神经网络模型进行比较，证实采用SVM进行键入特性验真的有效性，因而其具有广阔的应用前景。

COMPUTER KEYSTROKER VERIFICATION BASED ON SUPPORT VECTOR MACHINES

Traditional verification of the access to a computer system is single password. The drawback of the password is that it is easily given away. Therefore there are great security threats in the access to information and resource in the computer. In an effort to confront the threats, biometrics (such as fingerprints, keystroke dynamics, and so on) combined with the traditional verification methods may greatly increase the system security. In view of the fact that it is not necessary for the verification of keystroke dynamics to utilize the additional equipment and the realization is relatively cheap, after studying the past research on this issue, the keystroke verification based on support vector machines (SVM) is proposed in terms of SVM's super performance in pattern recognition. Through experiments SVM is compared with BP, RBF, PNN, and LVQ, and the conclusion is that SVM is the best choice in keystroke identification and therefore there is extensive application prospect about keystroke verification.