论文标题
保持和滑动：基于机器学习的基于触摸的连续身份验证模式
Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning
论文作者
论文摘要
近年来，存储在移动设备上的安全信息数量呈指数增长。但是，当前针对生理生物测量和密码等移动设备的安全模式不足以保护此信息。行为生物识别技术已被大量研究，作为解决移动设备安全性缺陷的可能解决方案。这项研究旨在通过评估使用触摸动态和电话运动的多模式基于行为生物识别的用户身份验证方案的性能来为这项创新研究做出贡献。这项研究使用了两个流行的公共可用数据集的融合，手动运动方向和掌握数据集和生物底数据集。这项研究使用三种常见的机器学习算法评估了我们的模型性能，这些算法是随机的森林支持矢量机，而K-Nearest邻居达到的准确率高达82％，每种算法分别针对所有成功指标都执行。
In recent years the amount of secure information being stored on mobile devices has grown exponentially. However, current security schemas for mobile devices such as physiological biometrics and passwords are not secure enough to protect this information. Behavioral biometrics have been heavily researched as a possible solution to this security deficiency for mobile devices. This study aims to contribute to this innovative research by evaluating the performance of a multimodal behavioral biometric based user authentication scheme using touch dynamics and phone movement. This study uses a fusion of two popular publicly available datasets the Hand Movement Orientation and Grasp dataset and the BioIdent dataset. This study evaluates our model performance using three common machine learning algorithms which are Random Forest Support Vector Machine and K-Nearest Neighbor reaching accuracy rates as high as 82% with each algorithm performing respectively for all success metrics reported.
