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
翻译:随着技术的成长和迅速发展,人们越来越清楚地看到,移动设备比以往任何时候更经常地用于敏感事项。在单一因素或多要素认证之后,需要不断验证用户,但需要不断作为单一因素或多要素认证,可能只是最初才对用户进行验证,如果假冒者能绕过最初的验证,则这种验证毫无帮助。随着技术的成长和迅速发展,触摸动态领域出现,作为不侵扰地收集关于用户及其行为的数据以实时制定和做出与安全相关的必要决定的一个明确途径。在本文中,我们提出了一个新数据集,其中包括追踪25个用户,每人玩两场移动游戏蛇子.io和Minecraft,每次10分钟,并附上相关的手势数据。我们从这些数据中学习机器的二元分类器,即随机森林和KNearest Neighbor,试图验证某一特定用户行动的样本是否真实。我们最强模型为两种游戏恢复了大约93%的平均准确度,显示触摸摸摸动能有效地区分用户,是验证计划的可行考虑。我们的数据集可以在https://github.com/zderididdidder/MC-Snake-Results。我们可以在http中看到。我们的数据设置在https-Sn-Results看到。