论文标题
通过基于实例的击键动力学的快速自由文本身份验证
Fast Free-text Authentication via Instance-based Keystroke Dynamics
论文作者
论文摘要
按键动力学研究用户通过键盘输入文本的方式。具有区分用户的能力，打字行为可以无效地形成行为生物识别系统的组成部分，以提高现有帐户安全性。自由文本数据上的击键动态系统先前需要500个或更多字符才能实现合理的性能。在本文中，我们提出了一种基于实例的图形比较算法，称为基于实例的尾部区域密度（ITAD）度量，以减少身份验证用户所需的击键数量。此外，发现击键动力学文献（例如专着和挖掘机）中常用的特征都可以在告知谁在键入中很有用。这些功能用于身份验证的有用性是使用随机森林分类器确定的，并在两个公开可用的数据集中验证。来自各个功能的分数融合在一起以形成单个匹配分数。凭借融合匹配分数和我们的ITAD度量，我们在Clarkson II数据集的100和200个测试数据集中达到了同等的错误率（EER），并提高了35.3％和15.3％的最新情况。
Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.
