Al-Mahadeen, E.; Alghamdi, M.; Tarawneh, A.S.; Alrowaily, M.A.; Alrashidi, M.; Alkhazi, I.S.; Mbaidin, A.; Alkasasbeh, A.A.; Abbadi, M.A.; Hassanat, A.B. Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data. Sustainability2023, 15, 10456.
Al-Mahadeen, E.; Alghamdi, M.; Tarawneh, A.S.; Alrowaily, M.A.; Alrashidi, M.; Alkhazi, I.S.; Mbaidin, A.; Alkasasbeh, A.A.; Abbadi, M.A.; Hassanat, A.B. Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data. Sustainability 2023, 15, 10456.
Al-Mahadeen, E.; Alghamdi, M.; Tarawneh, A.S.; Alrowaily, M.A.; Alrashidi, M.; Alkhazi, I.S.; Mbaidin, A.; Alkasasbeh, A.A.; Abbadi, M.A.; Hassanat, A.B. Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data. Sustainability2023, 15, 10456.
Al-Mahadeen, E.; Alghamdi, M.; Tarawneh, A.S.; Alrowaily, M.A.; Alrashidi, M.; Alkhazi, I.S.; Mbaidin, A.; Alkasasbeh, A.A.; Abbadi, M.A.; Hassanat, A.B. Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data. Sustainability 2023, 15, 10456.
Abstract
With the growing popularity of smartphones, user identification has become an essential component of maintaining security and privacy. This study investigates how smartphone accelerometer data can be used to identify users, and it makes recommendations for the ideal application parts. Accelerometer data from the HMOG public dataset was used to train deep learning, conventional classifiers, and voting classifiers, which were then utilized to identify users. To enhance performance, feature selection and pre-processing techniques were researched. The results show that RFE feature selection outperforms other approaches and that LSTM followed by XGBoost has the best identification performance as indicated by a relatively large number of machine learning performance measures. The proposed identification system nevertheless performed well and outperformed existing methods, which were principally created and tested on the same HMOG public smartphone dataset, even with a larger number of users. Further work would be necessary for such an application to reach its full potential, though.
Keywords
biometrics; deep learning; time series; feature selection; classification; accelerometer; Sustainability
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.