A Study on Feature of Keystroke Dynamics for Improving Accuracy in Mobile Environment

User behavior-based authentication, while providing convenience to the user, is not widely used in the real world due to its low accuracy. Keystroke dynamics is one of the user behavior-based authentication methods, and it has been studied for about 40 years. Conventional keystroke dynamics has used key timing features for the personal computer (PC) environment. Since the smartphone equipped with advanced sensors (e.g., accelerometer, gyroscope, and touchscreen sensor) was released, sensor-based features have been used to improve the accuracy of classifying users with key timing features. In this paper, we analyze the keystroke dynamics features in the literature and evaluate each feature to find efficient features. Based on tapping data collected from 12 participants, we evaluate the effectiveness of several features from the empirical data of a six-digit PIN. Our experimental results show that the feature Up-Up (UU), the time difference between releasing a key and the next key, and the min, max, and mean features extracted from motion sensor data have the best accuracy and efficiently classify each user.