TypeNet：按比例增加击键生物识别技术
TypeNet: Scaling up Keystroke Biometrics
摘要：
我们研究的适用性验证10万用户键入自由文本的动态。为此，我们首先分析我们基于Siamese递归神经网络（RNN）的方法在多大程度上能够在每个用户的数据量稀缺时对用户进行身份验证，这是自由文本身份验证中的常见情况。使用1K用户测试网络，人口规模与以前的作品相当，TypeNet获得了4.8%的相等出错率，每个用户仅使用5个注册序列和1个测试序列，每个序列50个密码。每个用户使用相同数量的数据，当测试用户数量增加到100K时，与1K相比，性能相对下降不到5%，这表明TypeNet在大规模用户数量下具有良好的扩展潜力。我们的实验是用阿尔托大学的数据库进行的。据我们所知，这是最大的自由文本击键数据库，包含来自168 K用户的超过1.36亿次击键。
英文摘要：
We study the suitability of keystroke dynamics to authenticate 100K users typing free-text. For this, we first analyze to what extent our method based on a Siamese Recurrent Neural Network (RNN) is able to authenticate users when the amount of data per user is scarce, a common scenario in free-text keystroke authentication. With 1K users for testing the network, a population size comparable to previous works, TypeNet obtains an equal error rate of 4.8% using only 5 enrollment sequences and 1 test sequence per user with 50 keystrokes per sequence. Using the same amount of data per user, as the number of test users is scaled up to 100K, the performance in comparison to 1K decays relatively by less than 5%, demonstrating the potential of TypeNet to scale well at large scale number of users. Our experiments are conducted with the Aalto University keystroke database. To the best of our knowledge, this is the largest free-text keystroke database captured with more than 136M keystrokes from 168K users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
