Abstract: In recent decades, mobile devices have evolved in potential and prevalence significantly while advancements in security have stagnated. As smartphones now hold unprecedented amounts of sensitive data, there is an increasing need to resolve this gap in security. To address this issue, researchers have experimented with biometric-based authentication methods to improve smartphone security. Following a comprehensive review, it was found that gait-based mobile authentication is under-researched compared to other behavioral biometrics. This study aims to contribute to the knowledge of biometric and gait-based authentication through the analysis of recent gait datasets and their potential with machine learning algorithms. Two recently published gait datasets were used with algorithms such as Random Forest, Decision Tree, and XGBoost to successfully differentiate users based on their respective walking features. Throughout this paper, the datasets, methodology, algorithms, experimental results, and goals for future work will be described.

Sara Kokal, Mounika Vanamala and Rushit Dave, “Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150302

@article{Kokal2024,
title = {Analysis of Gait Motion Sensor Mobile Authentication with Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150302},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150302},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Sara Kokal and Mounika Vanamala and Rushit Dave}
}