论文标题
使用机器学习和多指手机触摸动态的连续用户身份验证，并使用新颖的数据集
Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Dataset
论文作者
论文摘要
随着技术的增长和发展的迅速发展，越来越明显的是，移动设备比以往任何时候都更常用于敏感事项。需要不断地验证用户作为单因素或多因素身份验证可能只会验证用户，如果冒名顶替者可以绕过此初始验证，这将无济于事。触摸动力学领域是一种明确的方法，是非内部收集有关用户及其行为的数据，以实时制定和做出势在必行的与安全有关的决策。在本文中，我们介绍了一个新颖的数据集，其中包括跟踪25个用户玩两个手机游戏Snake.io和Minecraft 10分钟，以及他们的相关手势数据。从这些数据中，我们运行了机器学习二进制分类器，即随机森林和K最近的邻居，试图验证特定用户的样本是否真实。我们最强大的模型的平均准确度大约为93％，显示触摸动态可以有效地区分用户，并且是身份验证方案的可行考虑。可以在https://github.com/zderidder/mc-snake-results上观察我们的数据集
As technology grows and evolves rapidly, it is increasingly clear that mobile devices are more commonly used for sensitive matters than ever before. A need to authenticate users continuously is sought after as a single-factor or multi factor authentication may only initially validate a user, which does not help if an impostor can bypass this initial validation. The field of touch dynamics emerges as a clear way to non intrusively collect data about a user and their behaviors in order to develop and make imperative security related decisions in real time. In this paper we present a novel dataset consisting of tracking 25 users playing two mobile games Snake.io and Minecraft each for 10 minutes, along with their relevant gesture data. From this data, we ran machine learning binary classifiers namely Random Forest and K Nearest Neighbor to attempt to authenticate whether a sample of a particular users actions were genuine. Our strongest model returned an average accuracy of roughly 93% for both games, showing touch dynamics can differentiate users effectively and is a feasible consideration for authentication schemes. Our dataset can be observed at https://github.com/zderidder/MC-Snake-Results
