Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data.

With the increasing popularity of smartphones, user identification has become a critical component to ensure security and privacy. This study looked into how smartphone sensors' data can be utilized to identify/authenticate users and gives suggestions for the best application components. A public smartphone dataset was used to train a deep learning algorithms, conventional classifiers, and voting classifiers, which were then used to identify the users. Feature selection and Pre-processing techniques were investigated to improve the performance. According to the results, Recursive Feature Elimination beat the other feature-selection approaches, and Long Short-Term Memory (LSTM) had the best identification performance, as evidenced by a relatively large number of machine learning performance metrics. Even with a larger number of users, the proposed identification system performed well and outperformed existing approaches, which were primarily designed and tested on the same public smartphone dataset. In terms of user authentication, this study compared the effectiveness of accelerometer data against gyroscope data. According to the findings, the accelerometer data surpassed the gyroscope data in the authentication process. Notably, the study revealed that employing LSTM to combine the accelerometer and gyroscope data resulted in near-perfect user authentication. The insights gained from this study help to develop user identification and authentication approaches that employ smartphone accelerometer data. [ABSTRACT FROM AUTHOR]