关键词: 生物认证, 身份识别, 击键特征, 固定文本, 击键特征曲线

Abstract: Since the user’s typing pattern has been evaluated as a stable and unique characteristic to some extent, keystroke dynamics is a well known technique in security research. The traditional keystroke authentication methods usually use keystroke features such as the digraph latency extracted from two consecutive keystrokes and the keystroke duration. However, these traditional algorithms do not take the changing rate of any two adjacent keystroke features into consideration, which may degrade the performance of keystroke dynamics identity classification. In order to solve the above problems, a new concept of diversity degree of keystroke feature curve is firstly proposed in this paper and applied to the identity classification. The novel keystroke dynamic identity authentication algorithm not only uses the traditional keystroke features, but also introduces the changing rate of any two adjacent keystroke features, so it can recognize different user’s keystroke behavior much better than traditional methods. The experimental results demonstrate the effectiveness of the proposed algorithm, compared with several algorithms including Manhattan, Manhattan（scaled）, statistics, neural networks and machine learning.

Key words: biometric authentication, identity classification, keystroke feature, fixed text, keystroke feature curve