Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning
This addresses security for mobile device users by improving authentication, but it is incremental as it builds on existing behavioral biometric research.
The paper tackled mobile device security by proposing a continuous authentication scheme using touch dynamics and phone movement, achieving up to 82% accuracy with machine learning algorithms like Random Forest, SVM, and K-Nearest Neighbor.
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.