Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning

TL;DR

This paper proposes a novel continuous authentication method for mobile devices using touch and movement data, employing machine learning to improve security with accuracy up to 82%.

Contribution

It introduces a multimodal behavioral biometric scheme combining touch dynamics and phone movement, evaluated with multiple machine learning algorithms.

Findings

Achieved up to 82% accuracy across algorithms

Effective fusion of touch and movement data for authentication

Demonstrated potential of behavioral biometrics for mobile security

Abstract

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.