论文标题
一种新型的非线性转换基于固定文本击键行为动力学的基于非线性转换的多用户识别算法
A novel non-linear transformation based multi-user identification algorithm for fixed text keystroke behavioral dynamics
论文作者
论文摘要
在本文中，我们提出了一种新技术，以唯一对使用按键动力学访问单个应用程序的多个用户进行分类。当多个用户可以合法地访问共享计算机和帐户时，通常会遇到此问题，有时，一个用户可以在另一个用户的帐户中无意中登录。由于通常在此阶段绕过登录过程，因此我们依靠按键动力学来分开用户。我们的算法使用来自本地化的分位数变换和技术来对用户进行分类和识别。具体而言，我们使用一种称为基于序数的定位（UNLOC）的算法，该算法仅使用比较距离代理获得的顺序数据，该算法通过在降低的PCA/kernel-PCA/T-SNE空间中“定位”用户根据其打字模式“定位”用户。我们的结果在基准击键数据集的帮助下进行了验证，并表明我们的算法优于其他方法。
In this paper, we propose a new technique to uniquely classify and identify multiple users accessing a single application using keystroke dynamics. This problem is usually encountered when multiple users have legitimate access to shared computers and accounts, where, at times, one user can inadvertently be logged in on another user's account. Since the login processes are usually bypassed at this stage, we rely on keystroke dynamics in order to tell users apart. Our algorithm uses the quantile transform and techniques from localization to classify and identify users. Specifically, we use an algorithm known as ordinal Unfolding based Localization (UNLOC), which uses only ordinal data obtained from comparing distance proxies, by "locating" users in a reduced PCA/Kernel-PCA/t-SNE space based on their typing patterns. Our results are validated with the help of benchmark keystroke datasets and show that our algorithm outperforms other methods.
