TY - GEN
T1 - Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning
AU - Mallet, Jacob
AU - Pryor, Laura
AU - Dave, Rushit
AU - Seliya, Naeem
AU - Vanamala, Mounika
AU - Sowells-Boone, Evelyn
PY - 2022
Y1 - 2022
N2 - 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 multi-modal 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 (HMOG) dataset and the BioIdent dataset. This study evaluates our model's performance using three common machine learning algorithms; 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.
AB - 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 multi-modal 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 (HMOG) dataset and the BioIdent dataset. This study evaluates our model's performance using three common machine learning algorithms; 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.
UR - https://dx.doi.org/10.1109/CACML55074.2022.00081
U2 - 10.1109/cacml55074.2022.00081
DO - 10.1109/cacml55074.2022.00081
M3 - Conference contribution
BT - 2022 Asia Conference on Algorithms, Computing and Machine Learning, CACML 2022
ER -