Abstract
This research aims to further understanding in the field of continuous authentication using behavioral biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch dynamics can effectively distinguish users. However, further studies are needed to make it viable option for authentication systems
Abstract (translated)
这项研究旨在通过行为生物识别领域深入了解持续认证。我们为15个在三星平板电脑上玩《我的世界》的用户的每个动作数据创建了一个新的数据集，每个动作持续15分钟。利用这个数据集，我们采用了机器学习（ML）二分类器，包括随机森林（RF）、K-最近邻（KNN）和支持向量分类器（SVC），来确定特定用户动作的准确性。我们最健壮的模型是SVC，其平均准确率约为90%，表明触觉动态可以有效地区分用户。然而，还需要进一步研究，才能使这种认证系统成为可行选项。
URL
https://arxiv.org/abs/2403.03832
